In this episode, I sit down with Kyle Chan (Brookings Institution) to unpack the thinking behind his provocative New York Times op-ed, “In the Future, China Will Be Dominant, the U.S. Will Be Irrelevant.” We start with the DeepSeek moment and why it surprised the West, why it didn’t surprise many China-watchers, and why Kyle sees it as only “the tip of the iceberg.”

From there, we zoom out into the bigger story: China’s rise isn’t just one breakthrough model or one champion company. It’s a system of interlocking capabilities: EVs, batteries, renewables, industrial automation, robotics, and AI, advancing in parallel and reinforcing each other through spillovers, supply chains, and fast-moving “Swiss Army Knife companies” like Xiaomi and Huawei.

We also dig into what people often get wrong about China’s state role: not pure top-down command, but a mix of industrial policy + private-sector experimentation, including practical mechanisms like compute vouchers and local-government support. Finally, we cover India’s trajectory, geopolitical constraints, and Kyle’s “hedges”—scenarios in which today’s narratives (in both China and the U.S.) could still break in unexpected directions.

Relevant links: https://www.brookings.edu/people/kyle-chan/

In today’s world, there’s no shortage of information. Knowledge is abundant, perspectives are everywhere. But true insight doesn’t come from access alone—it comes from differentiated understanding. It’s the ability to piece together scattered signals, cut through the noise and clutter, and form a clear, original perspective on a situation, a trend, a business, or a person. That’s what makes understanding powerful.

Every episode, I bring in a guest with a unique point of view on a critical matter, phenomenon, or business trend—someone who can help us see things differently.

For more information on the podcast series, see here.

00:00 — The NYT op-ed + the DeepSeek catalyst: why Kyle wrote the piece, what he wanted to correct, and why DeepSeek was a wake-up call (“tip of the iceberg”).

06:53 — Kyle’s origin story: infrastructure obsession (high-speed rail) → the path into tech & industrial policy.

12:31 — China’s “electric tech stack” + spillovers: EVs, batteries, renewables, robotics, AI moving in parallel—and why “Swiss Army Knife” firms (Xiaomi/Huawei) can leap across categories.

19:12 — Why autonomy pairs with EVs: the technical and architectural reasons autonomous systems “almost always” sit on EV platforms.

24:01 — China AI ecosystem in practice: startups + hyperscalers + policy “tailwinds” (compute vouchers, industrial parks, local government support) and how that differs from the U.S. model.

29:46 — China’s development playbook vs others + the India comparison: proactive bottleneck-solving (“ground game”), plus India’s tailwinds and constraints over the next decade.

41:00 — The hedges + the wrap: what could derail or reshape the trajectory (trade backlash, geopolitics, bubble risk, robotics paths), and Kyle’s non-consensus take on policy intervention.

AI-generated transcript

Grace Shao (00:00)Hi, Kyle. Thank you so much for joining us today. Kyle, I want to start with your recent New York Times op-ed, which had a pretty provocative headline. It’s called, In the Future, China Will Be Dominant, the U.S. Will Be Irrelevant. When I saw that, I was like, whoa, this guy, someone’s going to go get blood now. How did that piece actually come about? What was the main, I guess, objective or goal out of that piece?

Kyle Chan (00:16)Yeah, yeah. Thanks for asking about that piece. Yeah, that piece, it got quite a reaction. I was surprised. And there’s been a number of pieces I feel like now—sort of, it’s almost become a genre of like all the things that China’s doing, all the things that the US is doing, the sort of divergent trajectories of the two countries, especially on technology. And for me, one thing I really wanted to focus on was—

So we had the big DeepSeek moment earlier in the year, and that really got people to wake up and take notice of what’s happening in China and China’s tech development in a way that really, I mean, I can’t remember the last time that something like that happened. And so that was quite a big wake up call. But as someone following a number of different sectors in China for a while, I was like, this is just the tip of the iceberg.

I mean, first off, within AI itself, DeepSeek—I think it was kind of funny—was surprising for a lot of people who follow actually China’s AI industry quite closely, because I think we might have been expecting some of the bigger tech companies to have made a bigger splash, but DeepSeek seemed a little bit out of left field. But within AI in China in general, there is so much talent, so much engineering talent, a vibrant developer community, top-notch researchers.

So like when you look at, say, who is accepted, whose papers are accepted for NeurIPS, one of the top AI conferences, right? It’s many, many, many names from Peking University or Tsinghua University or Zhejiang University. And so if you follow that space for a while, DeepSeek was not so surprising. Maybe it was surprising that it was DeepSeek itself, but that China could produce a world-class AI model on par or nearly on par with some of the best in the US—that was maybe not so surprising.

And then it’s not just AI. The other thing is like when you follow EVs or when you follow batteries, or if you follow anything related to clean tech—solar, wind, hydrogen fuel cells. If you follow robotics, anything related to industrial automation, industrial robotics, also self-driving cars, smart driving systems. I mean, the list goes on and on for all these different areas.

And then it gets even down to sort of like the basics, right? So like some of these traditional industries where you just see this like classic China chart. Call it like the classic China chart where it’s like the share of global manufacturing for shipbuilding, say, or steel—is like, at first you see like China is growing and then soon it’s like eclipsing the rest of world combined.

So to me, it was this bigger story that I really want to highlight: not just DeepSeek and not just AI China, but more broadly speaking, what is this bigger trend and why should we care? How is this going to shape not just Chinese society, but the rest of world?

Grace Shao (03:11)I think to your point, DeepSeek was very secretive, yet it wasn’t like it’s within the AI industry in China—people were already noticing it and people were talking about, I think maybe six months before even they came out with their first R1 and then V1. But I think to your point, yeah, it was a shot to the West because it was like, wow, we always knew that China had strong industrial capacities, right? Like you said, like we had the manufacturing capabilities, the factories and whatnot, the hardware capabilities.

They didn’t expect something like a software to come out of China that was almost on par with what they could produce in the West. I guess my question for you next is then to highlight that—what was your goal really? What was your real message that brought you public? Why did you publish an op-ed on the New York Times?

Kyle Chan (03:55)Yeah, so part of it was to kind of point to the underlying drivers for what was happening because I also wanted to kind of correct this image of China, not only in terms of like China’s tech development, but also what really was responsible for some of that.

So like the image I want to correct was basically this very old notion of China making, you know, low value added commodities like household goods, basic consumer electronics maybe—stuff that maybe is good for economic growth, but isn’t so impressive technologically and doesn’t really challenge, say, the US or Europe or other industry incumbents in these areas.

I want to first point out that, yeah, this is different China now. And this process has been unfolding for a long time, actually. So I was trying to highlight some of the efforts that the government was trying to do to help accelerate not just industrialization, but innovation itself.

This idea that it’s still so deeply controversial in the US—the idea that the government might have a positive role to play in supporting private sector development, supporting cutting edge technology—I think that that is still something that’s debated very hotly in the United States. And I wanted to point out how China has been able to use—not successfully every time, and there’s definitely issues along the way, but overall, quite effectively—it has been able to use industrial policy to really move the needle and support its industries and its private sector.

And so this combination too of like, it’s not just one or the other. It wasn’t just sort of all top-down state driven and it wasn’t just all sort of bottom-up private markets. It was this interesting combination that has produced, I think, these sort of like world beating industries.

And I think the lesson—a big part of the piece was about the US side of it and what lessons we might take away and how the US might need to step up its game. I don’t know if this competitive framing is the right one, but in general, a realization that, okay, there’s a lot happening. This assumption that China would always be the center of low-cost manufacturing and the United States would be the center of high-tech R&D, innovation, Silicon Valley—that the picture was much, much blurrier than that. So that was sort of like my overarching goal.

Grace Shao (06:27)I definitely want to double click on the part where you talk about how the state and the private sector actually work together. And we can talk about that later. But I want to get a sense on what kind of feedback or pushback or even maybe criticism did you get from that piece?

Then furthermore, I want to get understanding: how did you get involved in all of this? You’re in the US, right? You’re in New York, right? How did you get into studying China’s industrial policy? Tell us about your background.

Kyle Chan (06:53)Yeah, yeah. Yeah, I mean, I just recently joined Brookings based in DC, which is a DC-based think tank that has a really great China center that does outstanding research on policy issues related to China, US-China relations. And my focus now is on China’s tech and industrial policy.

But getting to this point has been like an interesting journey. So originally actually—I mean to go all the way back, I don’t know how far you want to go back—but like my family is actually from Hong Kong originally. And so I was born in California.

And my parents—it was sort of that generation where my parents really wanted me to learn Mandarin. They’re like, that’s gonna be the useful language. And it turned out to be very useful.

But also growing in California, I took cars everywhere. It was like a very, very much like a private transportation kind of city. And it was like a revelation to me—I mean, this sounds so ridiculous to anyone who’s grown up in a city with good public transportation—but it was like a revelation to me to later live in places like Chicago or even San Francisco and then later on Beijing and Delhi and Berlin, to be in places with like functioning subway systems and functioning public transportation.

So I got really interested in infrastructure, actually, not necessarily industrial policy. That kind of came later, but infrastructure. And here there is really sort of like a very strong role for the government to play in coordinating, if not actually building and maintaining infrastructure, whether you’re talking about roads, highways, bridges, railways, subways, electricity grids.

And I just found it really interesting then later on traveling to China—how this seemed to be like completely different there. I mean, and I remember at the time really being amazed by the high speed rail system there.

And China didn’t have a bullet train system for most of its, most of the existence of its railway system until basically starting in the 2000s. They started to take seriously this idea of like, okay, maybe we can like really roll out a nationwide bullet train system. And they did a lot of R&D.

And I became really fascinated by how they did this, how they built what ended up being, you know, within a decade, the world’s largest high-speed rail system, how they acquire the technology and how they built on top of that to create this sort of like truly like made in China kind of transportation system.

And then how they did that repeatedly, not just for high-speed rail, but like for regular highways, expressways, for, you know, any kind of infrastructure.

Okay, so that was sort of my foray into infrastructure and that was actually the focus of my dissertation. So I did field work actually for a number of years in China and also in India. I was based in Beijing and Delhi.

And really it was like an enormous privilege to be able to travel around often by train across those two countries trying to understand their systems. And the railways were really useful for like understanding not just the railways and transportation, but understanding deeper political economy questions, like the structure of the governments, how their bureaucracy works, what are the main issues with building a mega project of that scale.

And yeah, and so that for a long time was my focus. And then ironically, for an American audience, it was tougher to convince people that railways was interesting. I think most people were like, well, China can build high-speed rail because it’s the top down society and they just decide where to build and they build. I was like, no, no, it’s much more complicated than that. But it was hard to get traction.

But then I realized like some of the same tools and the same patterns, some of the same institutions in China were also involved in boosting and accelerating development in key industries. So I mentioned clean tech, electric vehicles and batteries and solar, but also traditional sectors.

And so I was really fascinated by this pattern that was, again, it kind of goes back to the New York Times piece. It wasn’t just one industry. It wasn’t just one company. It wasn’t just one state-owned enterprise, but this whole, like across the board effort to, you know, accelerate development overall.

So that to me was so interesting—this process of industrial upgrading, which many, many countries are interested in doing. And that actually got more traction in terms of like, you know, I joke that the U.S.—every country in a sense is a developing country, right? There are areas where we’re trying to improve and areas where we’re trying to catch up.

And I think now, you know, the question is like, what role can the right policies play in helping, say, the United States in a similar process? So that’s a long way of saying that it was—it was a long journey. But yeah, it’s an exciting time to study these topics because so much is changing. Every day it feels like.

Grace Shao (11:30)That’s really good context and I think, you know, for me how I found your work was exactly because you had such a high level kind of view, a bird’s eye view of everything that was happening and how you were piecing it together.

So I believe I came across one of your pieces on High Capacity, which is your newsletter on Substack for audiences who don’t know. You had this Venn diagram where you’re like, this is what China’s good at here, here, here, here, here. And this is what’s happening right now. And this is how it like actually relates to the current EV build out, the renewables, the AI, the robotics. It’s a very big ecosystem.

And in some ways you argue that, you know, all these different sectors operate in parallel, whether is, you know, a top down direction or a directive, or it was organic, you know, growth, but they did grow in parallel. So therefore they’re now able to kind of find synergy and leverage each other’s strength, right? With LiDAR sensors, drones, robotics, all coming together.

Could you tell us a bit about what that diagram really means? I’ll try to pull it up as well in the video. I think just help us explain that in a high level.

Kyle Chan (12:31)Yeah, yeah. So what I try to capture with that diagram was this idea that it wasn’t just one sector or wasn’t just one area, one technology that China had been able to grow and foster maybe through industrial policy or some kind of state support, but it was this combination of these interlocking technologies.

Now there’s a new term that’s coming into vogue, like the electric tech stack, or the tech industrial stack, or the electric industrial stack. And it’s really interesting because I think that really captures sort of this new paradigm that we’re entering.

Those technologies—you mentioned a number of them—electric vehicles, autonomous vehicles, which for various reasons we can get into are built on electric vehicles. There’s strong reasons why those technologies go together. Lithium batteries, which also feed into drones, autonomous delivery systems.

We can think about just regular consumer electronics, smartphones, but also more sophisticated robotics. I mentioned industrial automation as well. There’s a lot of overlap there.

And then the big circle overlapping all of these is AI—different models, software platforms that might intersect with say autonomous driving and, I don’t know, even sort of the humanoid robotics world.

So I just find it so interesting that China was making progress in a number of these different sectors at the same time, and progress in one sector would support greater development in another sector. So EVs and EV batteries grew up together in China.

So the development of, say, lithium iron phosphate batteries that were increasingly inexpensive, that were increasingly energy dense on a, say, kilowatt hour per kilogram basis, that were safer—those developments within the battery world made Chinese EVs more competitive and more attractive overall.

And then developments in China’s EV sector fed back into the battery world and also fed into other related sectors.

And then the other big thing is that I really wanted to highlight companies that lay at the intersection of these different areas. So I think probably right now, one of the hottest companies is Xiaomi, right? When I was younger, Xiaomi to me was inexpensive smartphones and relatively like affordable, like household products, like air purifiers and things like that.

And I think they had built up a brand and a very strong sort of customer base around this general idea. And what was so fascinating was seeing Xiaomi jump into electric vehicles and having such success with the SU7 and now the YU7 SUV, which both of which were like sold out for a long time and are very much in demand.

And they have like incredible features, they have incredible performance, and they have smart driving capabilities. And so it just sort of like showed like, wow, this company that was originally kind of like a smartphone company could make this shift over into the EV world.

And I would argue that it wasn’t just Xiaomi and Ledron’s entrepreneurship. I mean, a lot of credit goes to them for sure, but it was also because of this broader foundation that existed in China that allowed for this common supply chain ecosystem that would feed into these different worlds that would allow a company like Xiaomi to make that pivot.

And you see it again and again. I mean, now you see—and this is where I came up with this term like the sort of Swiss Army Knife companies—now you see these companies like XPeng get into like a whole range of industries, right? So not just EVs, but also humanoid robots, drones, flying cars even.

Huawei is probably the ultimate example of the Swiss Army Knife company, originally starting in telecom equipment, but then branching out into everything from sort of every aspect of consumer electronics—smartphones, tablets—into now AI chips. They’re a major player in semiconductors. For a while undersea cables. I mean, the list kind of goes on and on—EVs as well and smart driving, autonomous driving.

There was just this like burst of companies coming out of China that could do all these different things and branch out into new areas very, very rapidly. And it was like shocking to me how big some of these bets were, like Xiaomi making a multi-billion bet on a new already highly competitive industry, the EV industry.

But again, I think it all comes back to, in part, this foundation that was there in China—this like what I call these overlapping tech industrial ecosystems.

Grace Shao (17:17)I have so many thoughts I want to throw out. One is, I think to your point on Chinese tech companies going into EV, it’s really fascinating because to your point, there’s obviously this price war and this crazy competition in China.

But what I’ve heard from a lot of people who actually do purchase the Xiaomi cars and the Huawei cars and the Xpengs—they say that the technology itself is incredible. You have all the lights, the voice control, you have amazing AI-empowered functions. But that said, they’re not actually as good of a drive, like they’re not as smooth.

So like if you pick a traditional OEM like a Mercedes or BMW, their voice control usually apps the crap, to be honest. Like they go off—like you see the reviews online because we were looking at family cars and it was like the reviews were horrible. Like they just get triggered by really random sounds, they can’t pick up like accents, you know, they’re not really good with other languages.

And on the Xiaomi/Huawei/Xpeng/Zeekr side, they’re really, really good at this. But they’re not as good for driving yet. So it’s interesting that, like you said, what they’re good at in terms of day as the Chinese companies are the kind of technology that is quite recent and quite modern, but they’ve not really actually honed in on the craftsmanship or the, I guess, capability of building a really, really smooth driving car as Germans have—as they’ve honed the skill for over the last like four or five decades, right?

So it is interesting where they’re good at. And then in terms of EV, I had a question you mentioned earlier. Most autonomous vehicles are now—sorry—most EVs are now being tried for autonomous driving. Why is that? What’s the synergy there? Why can’t old OEMs actually have strong autonomous driving functionalities?

Grace Shao (19:00)Kyle, one thing we were talking about is the synergy between EVs and autonomous driving. Why is it that it’s better to actually build in autonomous driving functionality within EVs compared to like maybe traditional OEMs?

Kyle Chan (19:12)Yeah, so there it’s basically because you get a lot more control and precision. And you can have things like steer by wire where rather than sort of mechanical steering, you can have basically a signal be sent directly to the transmission or directly to the engine or directly to the brakes. So it’s sort of all.

And then on top of that, it’s helpful to have large battery capacity to handle sort of all of the different computing demands that would—including all the sensors that might be feeding data into the whole system.

So yeah, you basically—the two almost always go together: having autonomous robotaxis built on top of EVs.

Grace Shao (19:51)That makes a lot of sense actually. Okay, I never thought of it that way. You described China as building systems of capabilities and you kind of touched on this earlier with your Venn diagram. You talk about how China is not really just picking one winner or one winning sector, right?

So how does actually EVs, batteries, renewables end up supporting each other? And then how does that actually extend out and spill over into the AI era with robotics, physical AI, or even the consumer AI products we’re seeing out there today?

Kyle Chan (20:18)Yeah. So at one level, there’s an underlying driver here that’s almost not even specific to China per se. It’s something more about these technologies themselves and this broader convergence across them.

So, I mean, I pointed out Swiss Army knife tech companies in China, but to be fair, right in the U.S., you have companies like Tesla that are going into many different areas, or even Google with probably one of the world’s best autonomous driving companies.

So I think there’s something deeper happening here where there is this convergence of what we might have thought of sort of like lower end, like smartphone technologies or consumer electronics. Again, the lowly battery, right? Something so simple. This innovation in lithium batteries, making them cheaper, more reliable, and being able to scale up production—like that alone unlocked so much in terms of basically any kind of electronic device.

And so I think that’s why we are kind of seeing this emergence of this like whole new technology cluster.

And then for China, I think there is an awareness of the sort of synergies across different industries. And you can even go back further to China’s earlier industrial policy efforts, right? Take Made in China 2025, which came out in 2015.

And some of the target industries there were chosen not just because they might in and of themselves be useful or important, but also because they had broader spillover effects. You think about things like telecom equipment, IT infrastructure, anything related to energy, or anything related to communication in general.

Also CNC machines, right? So these are sort of like—they may not be like general purpose technologies in the way that electricity itself or computers are, but they might be sort of like multi-purpose technologies with broad applications in a range of different areas.

And so by making that bet on those types of technologies, you know, whether or not you succeed in becoming a global leader in that area, it will help feed into everything else that builds on that kind of tech stack.

So that’s what I see happen again and again, where for China’s approach to technology, where it’s not just about a single bet on a single technology, but trying to find these parts of the value chain that have large spillover effects and trying to support those—even if they in and of themselves might not be totally economically viable or the best businesses to invest in from a pure return on investment perspective, but they have these broader economic and technological spillovers.

Grace Shao (22:57)Yeah, and I think to your point on like a lot of the planning from top down, it’s not like they were just more strategic in like picking the right track.

But when I spoke to David Fishman a couple of weeks ago, he was saying China’s strategic planning on building electricity capacity is actually not because they foresaw like AI, the AI boom and data centers. You know, electricity is just simply urbanization actually was driving increase of energy demand as well.

They knew that in the future, a lot of things had to kind of move from traditional coal to renewable to kind of actually even have the capability to power what is needed of the future. So it was a grander vision versus just like, I know AI is gonna come in 10 years. I’m gonna build up renewable energy and the renewable energy is gonna help power data centers. So it’s not like there are profits or anything. So that’s interesting to hear.

I think I wanna understand how has that shift in ambition with like—

Kyle Chan (23:42)Totally.

Grace Shao (23:49)really China’s desire to move away from low wage, low margin, that trap into like higher value services and really like how has that shaped and driven the AI innovation that we’re seeing right now coming out of China.

Kyle Chan (24:01)Yeah. So I think what’s really interesting is, on the one hand, you have like a lot happening within the AI sector itself. You have obviously this like very vibrant ecosystem of startups, of big tech companies, even down to servers and data center construction firms, and even the major state-owned telecom operators involved in data center construction.

You have all these applications and developers trying to build on top of this whole set of foundation models, for example.

And that’s sort of just within the AI industry, right? And then on top of that, you have the fact that in all these other sectors, whether you’re talking about manufacturing, you’re talking about biotech, healthcare, there’s a lot of investment and progress in trying to move up the ladder in each one of those industries.

And so then you see people finding ways—either from those industries or from the AI side—trying to find ways to incorporate, to integrate AI.

And actually there’s something that I really love about your work where you’re highlighting those areas where it’s not just about like the latest benchmarks on the latest models and this sort of like endless race, but about like, how is AI like actually being deployed? How’s it being integrated into existing services and platforms in a way that would really boost, say, drug discovery or would really, I don’t know, improve tutoring and education services for students.

So I think that’s what’s so interesting here. And yeah, some of it might be supported by policy efforts. I think of some things like compute vouchers, for example, where startups—AI startups—might have trouble getting access to compute.

So we’re not talking about like the Alibabas and Tencents of the world. We’re talking about the little guys who may not be able to afford to build a giant data center dedicated just for them. And then local governments might offer compute vouchers—subsidized compute—basically access to public infrastructure, essentially, to help them sort of get off the ground and have that little bit to deploy on and develop with.

And so that’s an example of an area where you do have some government intervention stepping in and not trying to do it in a heavy handed way, but just trying to offer kind of like a tailwind of support.

And ultimately, and this is one thing that I think is sort of special about software and the AI industry in general is, I mean, a lot of this is sort of like, you know, this creative explosion of different ideas from the private sector—from all these entrepreneurs—like trying to look in areas that are related maybe to their own areas of expertise or just kind of like scouring: where can we plug in AI? Where can we make improvements, even very small ones into existing industries?

Grace Shao (26:45)That’s really interesting on compute bit, where I just met someone at Google in Singapore a couple of weeks ago, and he was saying that in some ways, the big tech in the West are actually operating in that capacity. Instead of incubating them and just taking another part of equity, and instead of just giving them capital, they’re giving them compute, essentially vouchers for these startups.

So I guess my question is, how do you see the relationship between startups and big tech in China versus startups and big tech in the West? And in what way, I guess, in what way do the state actually play a positive role or negative role in all of this in the whole ecosystem?

Kyle Chan (27:17)Yeah, that’s a question. I mean, in some ways it is a similar story, right? You have China’s own hyperscalers providing AI cloud computing services to a whole range of different players, you know, be they AI startups or, you know, large corporations or other, you know, maybe hospitals or other state-owned enterprises, for example. So that part might not be so different.

But I think what might be a little bit different is the sort of like extra on the margin support that the government in China—or especially local governments in particular—might offer.

Yeah, compute vouchers is one. Also these industrial parks where—and this is going back to like an almost an older model applied to like the age of AI—where, you know, AI startups, they still need office space. They still need help setting up a business. They might need help figuring out how to network with new customers.

And those are other areas too, where local governments in China might play a more active role in troubleshooting, trying to bring startups up to speed, trying to connect entrepreneurs.

And that’s something that I think is quite different than in the US system, where yes, you might have the large hyperscalers like Google or Microsoft providing that underlying infrastructure, that service for access to compute, but you won’t really see that kind of intervention or stepping in from the local government side, at least not so proactively by any measure.

Grace Shao (28:46) Yeah, I think definitely the West what you hear more about is like applying for fellowships or acceleration programs within whether it’s VC funds or like you said the big hyperscalers. Whereas even in Hong Kong here, like the Hong Kong government offers startups like staff support, back office admin sharing, teams to share, then like even offices in like Cyberport out like, you know, in Pok Fu Lam.

So like definitely the state plays a more active role and I think it’s kind of sometimes misunderstood by the West what that role means. It’s really many times it’s just like an incubator, a parent to someone, or even a mentor.

So we talked about you living in many, many different cities across the world and you study industrial policies across different developed economies. You mentioned that you lived in India, then you studied obviously China. So from that perspective, that international global perspective, what do you think China has actually done differently compared to maybe other developing nations that started in similar circumstances maybe say three, four decades ago?

Kyle Chan (29:46)Yeah, that’s a great question. So the thing that China has done that really has stood out to me, that really makes it so different than I would argue most other developing countries, is a very deliberate effort to basically build up industrial capacity and to move up the value chain.

It wasn’t like, you know, if we invest in education, if we invest in sort of these general factors that go into development, that over time, you know, you would eventually sort of get there. It was like: how can we bring in foreign companies to form joint ventures with our own domestic firms and share that kind of knowledge? How can we build up world-class research programs and build interesting scientific collaborations with the Europeans or the Japanese or the Americans even?

It was like: how can we try to like find those bottlenecks in the process?

And I think this kind of goes back to your point where it wasn’t like, this is the one direction we’re going to make this huge bet and that’s what turned out to be correct. It was more of these sort of— you know, to use like a sports analogy—like kind of like the ground game, right? It was like oftentimes kind of more tactical things trying to backfill areas.

Like let’s say for batteries, right? You need to have access to lithium. And so building a global network of lithium processing facilities and supply chains was really crucial to feed into EV batteries and then the EV industry itself.

And yeah, so I think that’s something that I see other countries do like a little bit, but really for China, it’s sort of at a very deep level: this effort to not just sort of hope that you got most of the pieces right and then let the story unfold, but to proactively find ways to support industrial development and technological development.

Grace Shao (31:44)What was actually, more personal, what was the most memorable thing living in Beijing and then commuting in New Delhi and then moving around the world so much over the last few years?

Kyle Chan (31:54)Yeah, I mean, there’s so many things. Yeah, I mean, I can tell you from a research standpoint, I interviewed government officials in both countries. And I can tell you that it’s much easier to get access to government officials, at least at the central government level, in India.

And so some of my sharpest memories are of long conversations over chai with railway officials in India, where I almost was kind of like a therapist for them in some ways, because they would have these complaints about the bureaucracy, about, frankly, their colleagues, about many thoughts about their country, about China, that they were just like very generous in sharing with me.

And it was like really fascinating to kind of like see the world from their perspective. I think in particular in India, there is a bureaucratic elite, there is a civil service elite who are highly educated. They often have to go through extremely competitive national exams to sort of like get to their positions.

And I think in some ways, I found this group of people to be both very proud, but also oftentimes very frustrated by some of the bureaucratic hurdles that they faced.

On the Chinese side, yeah, like when I did get access to government officials, sometimes even getting access, right, like it might take a while to get to like a genuine conversation where people can kind of open up more.

And it was so interesting because like I have my own theories about why say China was able to build high-speed rail so quickly, but it was always interesting to hear people’s own perspectives. To see like, what did they think was like really, you know, the thing that moved the needle.

And I heard like sort of everything from, you know, that China is just a much more sort of like coordinated and aligned system across the board to cultural explanations to, I mean, you name it. But it was always fascinating to hear like from people within the system.

So like to the extent that I was able to get that, and then I got to visit—I got to actually visit like the construction sites for like ongoing railway projects. And that was really cool.

So yeah, I mean, there, there’s so many. Like the two countries are so fascinating and you could spend a lifetime in either one and feel like it’s not enough, in terms of exploring and getting to see different parts.

Also, I did get to travel a lot. And like India, I don’t know if many people know this, like India has like the whole Northeast region, which is sort of like very distinctive culturally in terms of geography. It feels very different from the rest of the country.

And, you know, like you could explore that whole area, or you could travel to the South and there’s like a big sort of North-South cultural divide that anyone from the North or South will tell you about.

So yeah, it’s just like—I mean, these are sort of like continental-size countries. And I think they have that kind of continental-size complexity to them.

Grace Shao (34:51)

Definitely, I would love to visit India one day.

Do you think what we’ll see India kind of become the next China, if you must put it that way? Because for many years people said, oh, Vietnam’s going to be the next China, right? And obviously all the talent, all the capital, all the interest in that right now moving to India, as well as even a lot of the supply chain, right? For a lot of the big companies in Europe as well as the US, what should we expect of India in the next maybe decade or so?

Kyle Chan (35:14)Yeah, that is like the big question. And I will highlight some factors that are very much in India’s favor, and then I can point out a few challenges.

So in general, the India story is like really fascinating because if it weren’t for China’s high growth story—like if you kind of remove that from the equation—India would have been the envy of the world.

I think in many ways they have already proven to be able to have high rates of sustained growth over multiple decades now. So like in that sense, they’re already getting there in some ways.

The other things that are really working in their favor are: there is a big focus on manufacturing, on trying to build up industrial capacity in a way that is very reminiscent of China. There’s a huge push in industrial policy targeting areas like consumer electronics, the automotive slash EV industry. There are big ambitions for the semiconductor industry in India.

And the third thing I would point out is that there, up until recently, was a bit of a geopolitical moment for India with different companies trying to move away or diversify from China to some extent, diversify their supply chains.

And this was really quite an opening for India where you saw, like for example, Apple was able to shift a fairly large share—I think up to like a quarter—of its iPhone assembly was shifted to India. And with that also began a process where some of the suppliers would start to follow. And so I think that was actually honestly, to me, surprising how quickly that happened.

So those are some factors in their favor. But on the other hand, there are some very deep structural constraints. And one is that the bureaucracy, especially around anything related to labor, is very, very tricky to navigate.

And I think for companies who want stability, who want sort of like a certain stable business environment, it’s harder to operate, and there’s a lot of regional variations. So some of the southern states like Tamil Nadu, Karnataka tend to be seen as more business friendly, open to foreign investment, open investment of many different kinds. And that is where you see the electronics industry really growing quickly.

But overall, there are some bureaucratic issues. And then at a more fundamental level, something that China has been able to do is often reform its own internal organizational structure—sometimes pretty quickly, sometimes shockingly fast.

And sometimes it takes a while, like the railways did take a while, the railway ministry. But for India, I think there are some problems that everyone knows exist in a policy sense, but nobody knows how to address and break, say, like a political deadlock. That happens a lot. It probably also happens—I mean, I know it does happen in China as well. But I do see it more clearly and especially in the areas that India is trying to target for growth.

Grace Shao (38:10)And do you see actually a lot of the Chinese companies we talked about earlier, like the Xiaomis, BYDs and Huaweis of the world—are they exporting to India or even exporting their know-how and their supply chain and manufacturing? Because I know a lot of these Chinese companies have been doing that, like moving their talent, their know-how and also selling to consumers in Southeast Asia. But what is kind of the South Asia market looking like for these Chinese companies?

Kyle Chan (38:35)Yeah, I think from the Chinese companies perspective, they would be eager to reach one of the largest markets in the world, right? And then South Asia more generally.

And I think you see other areas like Pakistan and Nepal—Chinese, say, EVs or other smartphones or other consumer goods—like really taking off.

India and China specifically, though, have a bit of rocky geopolitical relationship. And so that definitely cast a chill over cross-border investment and business flows for a while.

And more recently, it seems like things are warming up, maybe gradually, and I think this is taking years. But I think there’s a recognition, certainly from the Indian side, that it helps to bring in that expertise and know-how—to have Chinese engineers, technicians, and managers participate in India’s own industrial development process.

I think, I don’t know if we’ll see those days again when you had, say, Alibaba invest like, you know, hundreds of millions in PayTM, which was India’s sort of equivalent of Alipay. I don’t know if we’ll see those days again, but maybe there is a sort of warming up of relations between the two countries and we might see greater sort of business flows following that.

Grace Shao (39:48)Yeah, because it seems like India has their own ecosystem of like FinTech as well as their own ecosystem of social media. So it’s not like they really need China’s software export.

But I think on the hardware side, it was really interesting reading Patrick McGee’s book and he was saying how back then it was the Taiwanese and the Americans that actually had to—well, the Americans first trained the Taiwanese and the Taiwanese came to train the Chinese.

And now it seems like if the supply chain is moving over to India, I’m sure the business will be business and, you know, it would make sense for the business to actually have the Chinese now train the Indian factory workers to really take over that new labor demand, right? Yeah, but geopolitics aside, that is.

Let’s talk about AI quickly. You know, in your conversation with Heath Yap—also I love his show and a good friend of mine—really grateful for Keith connecting us actually.

In your conversation with Keith, you said that you’re always careful to hedge. I kind of chuckled when I heard that. You’re talking about China’s rise and China’s rise in AI. You’ve obviously been quite vocal talking about how China has done some things right. But yet, obviously, as an American, you are saying it from a perspective of what we can learn as Americans.

And what are the scenarios where your own projections—China’s technological rise—don’t pan out or that could derail this story, right?

Kyle Chan (41:00)Yeah, that’s a really good question. So there are some ways that this story can turn out differently.

I do think that China has been able to benefit for a long time from especially international partnerships. And I think the extent to which there might be skepticism or even suspicion about those sort of international partnerships—that could affect, say, research collaborations or business partnerships across border.

At the same time, though, I guess at this point, I can point to areas outside of AI where there is a lot of interest, say, from German or European automakers in partnering with Chinese ones, trying to learn some of that know-how and technology. But that’s a big wild card.

And then another big one is also the extent to which there is a bit of a trade backlash, right, to Chinese exports and to what extent other countries might push back and maybe put up more tariffs or maybe even just demand greater investment and localize manufacturing in their own countries. So that could change things, not necessarily in a worse way, perhaps, but it could change things in some ways.

And then I do think that the relationship with the US is really crucial. And I think for China’s development, the instability in the relationship—I mean, it doesn’t help anyone, I think. So that’s sort of another wild card in all this.

And then from the US side, I mean, it’s sort of interesting to think about American AI development right now, because I literally like every day my feeds are like talking about bubbles and talking about like, you know, circular investment deals and things like that.

And at the same time, like there’s still so much enthusiasm and the data center build out is like seems to be still going full blast. So here’s like where I will offer like a hedge perspective where it could go in many different directions.

It could be a bubble and we might see a pullback and it might turn out that these investment deals just don’t pencil out. The economics just don’t work out.

But it could also be that we’re not—that this talk about AGI, I don’t know if like AGI per se will be achieved—but that broad-based artificial intelligence like computers that can do a huge range of tasks, not just coding, which is already impressive, and not just writing and things like that, but really sort of a broad range of tasks—that could take off.

And the United States huge investment in compute capacity could give it an edge in the long run.

So those are different ways things could pan out. And then the other wild card is like embodied AI and robotics and how that will pan out.

Because I think there’s like different approaches being taken by different companies, by different countries. I think some companies are looking to a version of AGI for robotics, kind of a universal foundation model for robotics that can operate across any different sort of hardware platform.

And other companies—I think about like Unitree—are focused on just deploying fast, like building tons and tons of like quadruped and humanoid robots now that have become kind of like the new hardware platform for many robotics developers, including in the United States.

So there’s many different ways things can go.

And then like more recently, there’s like, I have this tweet about like highlighting like street cleaning robots and just like very practical, like immediate real world use, right? So that could be another area where, you know, maybe we won’t get the AGI robots, but maybe we will have many, many different types of robots doing more specialized tasks.

And that could be transformative for either country. These are all areas that I’ve followed closely. Yeah, at this point, if I had a crystal ball, maybe I would be in a different job.

Grace Shao (44:57)

I think he definitely touched on something I feel like it resonates with me. Like is when I speak to people in the US—not really just the West, but really just in Silicon Valley—I feel like exactly to your point, there’s such a polarized kind of take on where AI is going.

It’s either it’s such a bubble or it’s going to burst. It means nothing. This is complete fad, right? Or it’s—or even, you know, people are like, this is going to ruin our lives. We must stop it right now.

Or it’s a very polarized view, which is like, OK, this is like the best thing that’s ever happened to us, like, you know, AGI or nothing.

And in China, when people ask me about what’s happening on the ground, it does feel like—I’m not sure pragmatic is actually the right word anymore. I don’t like using that word anymore because I feel like it’s now overdone. But I do feel like it’s a bit more, I would say centered.

As in everyone kind of feels like it’s just another wave of technological advancement. Whether how it might pan out—to your point—it might not be AGI, it might not be this crazy intelligent being that will take over our thinking capabilities, but it might be something that will transform how we humans interact with each other, how we work, how we actually increase productivity.

And I think it might have some—it might lean on the fact that frankly China really did see this kind of technological advancement driving productivity gains only just recently, within the last generation. Whereas in the US, you haven’t really seen this kind of crazy gain for the mass in a while, right? Since the Industrial Revolution. So people don’t feel it as much and there’s more fear around it.

Now I have another question on this. Just to kind of wrap everything up: you look at a lot of the industrial policies and you look at how the state works with the private side in the AI sector right now in China. How much are we seeing that is actual genuine market pull versus state-driven push?

I think you kind of alluded to the fact that like Chinese government does work with helping talent and raising capital. But right now with the big names—like the Unitrees, the UP techs, the deep CXC, Galabots and the whatnots, right?—that we’re seeing, are the state behind this or like the West often seems to think that way. How do we understand that?

Kyle Chan (46:56)Yeah, so I think it’s a complex relationship. So I don’t think it’s sort of like a top-down, the government is sort of like dictating what direction these companies need to take.

And I think there are probably national priorities where I think Beijing or local governments would want to see companies focus on more.

And ironically, like there’s a very strong convergence right now on wanting to make progress on AI more generally from the public and the private sector.

And then I do think that some of it comes down to problem solving on the ground. So this is something we talked about earlier: trying to figure out what issues the private sector is running into, what issues certain firms are running into, and then trying to troubleshoot and support them in some of those areas.

And then, yeah, overall, sort of like offering this broader roadmap or this broader set of like this broader package of sort of like a policy support, whether it’s sort of like a robotics-specific national strategy plan.

And part of it is just about even signaling that there is state support for these sectors, that these are areas where if you venture in as a private business, like you will not have all your problems solved, but you could at least have ways to have some of your sort of like more day-to-day issues addressed.

Grace Shao (48:17)Kyle, if you look at the AI sector today in China, how much do you think we’re seeing genuine market pull versus state-driven push? And especially now we’re also seeing the AI plus policy being pushed out. Is that diffusion driven by the state or is it driven by the actual entrepreneurs, especially amongst the Unitrees, the UP techs, the DeepSeeks and the MiniMax, Moonshots of the world?

Kyle Chan (48:39)Yeah, so that’s a great question. And I think what’s interesting about this moment is there’s a strong alignment between the public sector and the private sector, where clearly Beijing at a national level, and then many of the local governments want to see a booming AI industry in China.

They want to see China as a whole make progress in applying AI to all different parts of life—support economic growth and improve social services and improve education and all these good things.

And at the same time, you have so many different private sector firms from the big tech companies to the smaller startups really jumping in and also eager to innovate.

And I think one thing that’s really sort of exciting about all this is you can really like drill down into like subsectors or different aspects of China’s AI industry and just see how vibrant the startup ecosystem is now in terms of all the different, say, new coding tools that are emerging or different efforts to try to expand overseas or interesting ways to integrate AI into e-commerce or social media or EVs or advanced manufacturing—you name it.

And so right now, I just think that there’s just like this huge sort of creative explosion of different ideas and an eagerness, especially in this sort of post DeepSeek moment, to try new things and take that risk.

Grace Shao (50:04)Super interesting. Kyle, I really appreciate your time today. And for listeners, we actually had a few technical glitches today. That’s why we’re going to have to chop up the conversation a little bit. It might sound a little jumpy than my usual podcast.

My last question for you, which is a question I ask everyone: what is one differentiated view or non-consensus view you hold? I guess your New York Times piece was a pretty strong opinion and you really publicly put it out there. But is there anything else you think that, you know, you think differently or you have a different kind of view on that compared to your peers maybe?

Kyle Chan (50:35)That’s a question. I do think in general that my take—maybe it’s becoming more mainstream—that there is a role for very creative and very thoughtful policy intervention in supporting strategic sectors, in boosting technological innovation.

I think that’s something maybe perhaps widely accepted in other countries, but at least in United States, still something that a lot of Americans are not quite comfortable with.

And there are certainly risks along the way, but I think also to keep in mind, there are risks of not doing anything and of not trying to support R&D, scientific development and all these different things and to leave it purely to the market. That’s sort of my slightly left field take.

Grace Shao (51:21)Thank you so much, really appreciate your time and your insights again. Thank you, Kyle.

Kyle Chan (51:24) My pleasure.

Most AI policy conversations still orbit around Washington and Brussels, but Asia-Pacific is already writing a very different rulebook. In this episode, I talk with George Chen, Digital Partner at The Asia Group and former Meta policy executive, about how AI is actually being governed, built, and deployed across APAC, China, and the global south.

George traces his own path from journalism to big tech to advisory work, and uses that vantage point to explain why APAC is not “one market”—and why the EU analogy breaks down almost immediately. Countries like Japan, Korea, Singapore, and China are leaning into AI as a tool for economic recovery and industrial upgrading, often taking a much more pro-innovation, pro-growth stance than the EU’s more precautionary approach. At the same time, Southeast Asia is becoming the physical backbone of the AI build-out: Singapore as HQ and regulatory hub, with Malaysia, Indonesia, Thailand, and the Philippines hosting the data centers, power, and connectivity—along with all the local tensions that come with that.

We also get into what “responsible AI” actually looks like inside a company. Beyond the buzzwords, George breaks it down to three pillars—security, safety, and privacy—and talks through how mature players like Microsoft or Meta build these into product design from day one, versus the reality for startups trying to ship fast with one lawyer and a single policy person supporting multiple markets. He also makes the case that fragmented regulation and the lack of international standards are becoming a real tax on innovation, especially outside the US and EU.

Another big thread is the emerging US–China competition over AI governance itself. It’s no longer just about who has the best models or chips; it’s also about who exports their rules, norms, and defaults to the rest of the world. The US is pushing an “America-first” innovation and safety model to allies, while China is pitching AI as a kind of public good to the global south—combined with a more cost-efficient, top-down deployment model and very strict cyber and real-name rules at home. George argues this divergence is already shaping how content, deepfakes, and AI-generated media are treated in different jurisdictions.

We talk about the local edge of Chinese models—why in places like Beijing, models such as DeepSeek can be more useful than ChatGPT or Gemini for everyday queries because they’re trained on more localized, timely data. From there, we zoom out into the new AI talent map: countries like Indonesia, Vietnam, Kazakhstan, and Uzbekistan trying to position themselves as low-cost AI talent hubs and “back offices” for global AI companies as coding gives way to prompting and applied ML.

We close on a more philosophical note: should AI be built as a subordinate assistant or a true partner? George shares his uncertainty here, and we talk about what happens when we give AI more agency, emotional intelligence, and continuous workloads. At some point, the conversation shifts from safety checklists to ethics, culture, and even “digital colonialism”: whose values, whose norms, and whose worldview are encoded into the systems that end up mediating how we see the world.

In today’s world, there’s no shortage of information. Knowledge is abundant, perspectives are everywhere. But true insight doesn’t come from access alone—it comes from differentiated understanding. It’s the ability to piece together scattered signals, cut through the noise and clutter, and form a clear, original perspective on a situation, a trend, a business, or a person. That’s what makes understanding powerful.

Every episode, I bring in a guest with a unique point of view on a critical matter, phenomenon, or business trend—someone who can help us see things differently.

For more information on the podcast series, see here.

AI-generated transcript.

Grace Shao (00:00)

Hey George, thank you so much for joining us today. I’ve been really excited and waiting for this chat. You know, you are a very busy man. You’re constantly traveling. I can barely reach you in Hong Kong. So really appreciate your time today. Sit down with me and share your insights with my followers and some of our listeners. To start with, you’ve worn many, many hats. A journalist, tech executive, policy advisor, and now a partner at the Asia Group where you advise a lot of force, you’re probably helping companies on, I believe, geopolitical positioning, right?

George Chen (00:29)

Thank you. First of all, thanks for the invite. It’s quite an honor to join a growing cohort of guests for your program. Really happy to have a discussion about tech and policy issues because I think you’re right. My first 10 years in media, similar to your background, and most recent decade, I work very much on the intersection between technology and policy.

My biggest takeaway from my last job at Meta, one of the platform operators in the world, is sometimes we very much focus on technology development, like the breakthrough, while the resources for policy support are actually quite limited, especially in the Asia-Pacific region compared with the US. think for all the...

Big tech in the US, given the politics domestically, they have to do a lot on political and policy part. But for Asia Pacific, the policy work, compared with other investments, like in data center, technology, hiring of engineers, it’s still very, very, very understaffed, under-resourced, and sometimes under-appreciated. This is why we need to...

address some concerns about policy issues as we advance the technological part. Because I always tell my students, tell my friends, tell my partners that the key challenge, even you have CharGBT 5.0 or 6.0, the key challenge is how to get the government to understand new technologies and also get the users to have more trust in those new technologies. Otherwise, nobody use it, nobody trust those things. And that makes them.

Grace Shao (02:15)

I think that’s super helpful. A lot of times when we think about policy or safety issues, we think about it as like a siloed part of the ecosystem. But really like exactly to your point, like, you know, we need the developers to understand the concerns of the users. We need the users to understand the safety risks of the products. We need the regulators to understand what it means to implement these like technology throughout our economy, right? So there’s it’s like, it’s actually all interrelated.

I think today to start off with, let’s like go into big tech, just give in your background with Metta, working with a lot of these big tech companies. You’re based in Hong Kong for the listeners, but actually work predominantly for American big tech companies. What is like the, I guess, the fundamental feel right now as we see the evolution be from a social media company for AI to AI of focused company as this is now the forefront of their strategy.

George Chen (03:11)

Right, so for the Asia-Pacific region, it’s big. I always try to explain to my clients and friends, when people talk about Asia-Pacific, the first gross perception, perhaps from Western perspective, is, okay, treat Asia-Pacific like the EU, right? But EU is a single market. They have very much shared the language, English, also one currency and they have the European Parliament to pass legislation for EU member countries. Asia-Pacific is far diverse, far different, and much bigger. So it’s hard to just copy whatever works in EU and then let’s also do it in APAC. Using AI regulations as a clear and classic example, you know, you is the first You know government, you know to have the world’s first AI act, right? But the so-called the Brussels effect didn’t really happen this time in Asia Pacific countries You didn’t see like all the countries, you know, like Singapore or you know Japan to quickly follow up on You know to have a similar like a risk-based approach or penalty focused approach to AI, right? Instead, you know if you look at Japan. They are very much welcoming. Japan declared to be, they want to be the most friendly open country for AI developments. The first data exception for AI testing was actually in Japan. And then Singapore followed, and Hong Kong’s also not considering, right? So APAC took a very different regulatory approach to AI versus EU. I think this is something all the American tech companies have to realize. It’s not like America leads technology and then EU matters because of the special relationship between US and EU. So as I mentioned at the beginning, the resources for public policy work are very limited in AIPAC, but EU still enjoy a lot of resources, this English-speaking market that has lot of political connections. And then Asia-Pacific, when it comes to policy enforcement, like policy support it feels more like a third country, overall speaking Asia-Pacific as a whole. So there’s still a lot of educational process, the learning curve for big tech, largely from the US to understand what are the challenges, what are the opportunities in the Asia-Pacific market. However, I also need to highlight for many big platforms, Asia-Pacific is actually not just the largest market by internet users for American tech companies, for almost for all of them, right? You know, in terms of user base. It is also a very important revenue source, know, the source of revenue for those American companies. So now you see the imbalance, right? You you make a lot of money from Asia-Pacific, but the support you give to Asia-Pacific is quite limited, know, compared to in the US ⁓ and EU. So the learning curve is there.

American tech companies want to have a more sustainable development and want to have a more constructive relationship, sort of a more constructive partnership with Asian governments. I think there’s still a lot of work to do.

Grace Shao (06:31)

I think that’s really helpful to help listeners understand because sometimes people also approach me, they’re like, what’s APAC? I’m like, APAC is gazillion different markets and it’s actually so fragmented, right? And I think people sometimes misunderstand it kind of similar to like what you said. They think it’s like a EU. It’s not like actually there’s no consistency in currency. There’s no consistency language or no consistency actually even income or anything. So it’s quite scattered. that sense, I actually want to ask you, you mentioned something just now.

George Chen (06:39)

That’s right.

Grace Shao (06:58)

Japan and Korea this time is taking a more proactive actually approach as the countries themselves are taking more proactive approaches to really embracing AI and you know actually compared to EU’s more wait and see or more protective measures right which is not very yeah not not not what they usually would do what do think the trade-offs are actually in that sense do you actually think that means we are seeing more innovation or more technological breakthroughs or even economic diffusion of the technology right now in Japan and Korea.

George Chen (07:30)

Yeah, yeah, let me put it this way. So AI technology, you know, we believe, you know, still in the very early stage, right? Even you talking about, you know, trying to redefine Polisero, you know, but, you know, if you put that in the overall development for AGI, you know, we are still very much under the, in the early stage of the curve. So for Asia Pacific region, yes, it’s diverse, you know, but we can still see some sort of patterns, similarities in terms of different AI strategies. At the Asia group, my firm, we did a research paper on the different regulatory approaches to AI governance in the vast Asia-Pacific region, from Australia to even in Mongolian. Long story short, you are right. Some countries in Asia-Pacific take ⁓ a more economic benefit focused approach, right? Take a more innovation focused approach. Countries like Japan, Korea, Singapore, they want to see how AI can help them to drive economic impact, right? It doesn’t mean like they don’t care about the safety, the security issues, but they want to have certain flexibility, to encourage more startups to succeed, right?

in to a certain degree, actually maybe too many surprising because China is very well known as one of the strictest internet market in the world. Basically, none of the American, very few, I will say, like very few American tech companies can really succeed in China. The only two exception in my mind are like Tesla and Apple. But they are more like consumer related if you touch on content.

We talk about Google and Meta, that’s a completely different story. But even so, China at this time is also taking a more pro-innovation, pro-economy approach to AI development because this is a very top-down approach because President Xi saw the success of DeepSeek and he basically wanted more success stories like DeepSeek. Japan and Korea are in more or less the same category, like pro-innovation, pro-economic recovery. For Japan,

I talked with my friends and colleagues in Japan. The sentiment in Japan is like, we’ve lost 30 years, guess, three decades in terms of economic recovery. This is like our last chart. And Japan has been quite strong in robotics, those fundamental technology development. So that’s the sentiment in Japan. We have to grab the AI opportunity. In EU, have to say, part of the reason why EU is so keen to develop regulations, legislation in recent like five to 10 years. In my view, some may argue and disagree. I think the EU does come with a sense of protectionism, right? Because if you look at all the market leaders, you name it, OpenAI, Google, Microsoft, AWS, all of them are big tech from America, right?

I remember there was a chart to list the top 10 most advanced AI models. There’s only one model from EU, actually from France. The rest are from the US and China. So that tells a lot. If you are the EU regulators, look at from a competition perspective, you will more or less have a sense of anxiety. And then you will look at all those big tags like, no, we need to do something, like a country that pays in the name of safety and security. I’m not blaming EU regulators for doing it. But in the meantime, we also hear more and more concerns, even from the state heads, like French President Macron. He’s concerned that tough regulation in EU on AI will harm innovation in the EU rather than help European startups.

Grace Shao (11:14)

I think we can double click on China later. It’s going to have its own special segment for sure. China is just such a big story. But for some context for lot of listeners, Meta and Google, the likes of these companies actually do exist in mainland, but they mostly only have their ad services there. So basically they help enterprises with their ad sales to the West. But to Georgia’s point, they’re not really operating at the full capacity that you would see them elsewhere in the world.

George Chen (11:33)

That’s right.

Grace Shao (11:39)

Now I do want to kind of finish up on the APAC kind of narrative and then the APAC focus right now, which is for ASEAN right now. Let’s set apart like South Korea and Japan and China, just the Northeast Asian countries are frankly economically much more, you know, like developed as well as more economically focused, right? For ASEAN right now, especially since I just went to Singapore last week, it’s really interesting. Like we basically have the players, like you said, OpenAI, Google, Meta, all of these. Well, APAC headquarters based in Singapore, even the 10 cents and the bite dance of the world, right? However, Singapore is tiny, like just in terms of size and its resources. So what we’re seeing is they’re extracting essentially all the compute energy data centers, connectivity, any of the infrastructure you need to think of actually to Malaysia, in Malaysia, in Indonesia, in Thailand, even they’re building them out over there. How do we actually understand this right now? Is this a net benefit for these economies? Or is it actually really hurting the local economies and, you know, in some ways exploiting them and really just only serving the companies based out in Singapore? How do we understand that?

George Chen (12:45)

That’s right. So let’s talk about Southeast Asia. It’s complicated. When we’re talking about APEC, actually the most complicated part, I think it’s like Southeast Asia. Because when we talk about Korea, Japan, China, even China is a socialist country, but in terms of economic models, there’s a lot of elements related to capitalism. So those are the most economic economics in the Northeast Asia. Southeast Asia is very diverse, very different from each other.

Singapore is like the exception, the most advanced economy in South East Asia. But they come in terms of population, the user base is pretty small, like 4 million, 5 million population, even smaller than Hong Kong. You’re right, a lot of the tech companies, even before AI become a trend, they talk about like Meta, Google, Apple, they all had their headquarters in Singapore. It has really become the hub for big tech over the past 10 decades. Unfortunately, Hong Kong, thank God that we still have big banks like JP Morgan, Goldman Sachs in Hong Kong, we remain as a financial center. But in the aspect of tech innovation, you have to give some respect to Singapore. They did very well to attract those tech headquarters. So this also became, you are right, sort of a point of

I don’t know how to describe it. Some of the neighboring countries are jealous, certainly jealous of the success in Singapore, right? And then countries like Indonesia or Malaysia also wondering like how to get the benefits from the fact that all the big tech have their headquarters, regional headquarters in Singapore, right? But if they only care about the relationship with Singapore or in government, because they have headquarters in Singapore and their neighboring countries will not get any benefits, Malaysia actually founded their own ways in the regional AI race. And their offer is data center because of the stable supplies of electricity, relatively much cheaper labor costs and land costs and overall cost for data center operations. So this is why Malaysia got a lot of attention from Big Tech too, like AWS, Microsoft, they all made huge investments in Malaysia. Not AI, R &D, maybe yet, but first our data center. In the AI industry, we have a popular saying that AI is like electricity. Sam Altman said that. Basically, this is like the new kind of utilities for everyone’s life, right? But to develop AI you also need electricity. You need a lot of investments in infrastructure. This is why Malaysian already stand out and Philippines too in a way, as sort of the cheap, reliable alternative to data center investments in addition to Singapore. Everybody complains about Singapore in terms of living costs, even like how difficult it is to get work committed in Singapore these days. Even you have a call like qualify the job, but it doesn’t mean like you will get work permit immediately. Actually, in comparison, Hong Kong is doing quite well to attract the talents more easily these days in the tech and financial space. Back to the AI governance issue, yes, Southeast Asia also took a very different approach in comparison with Korea and Japan. think Singapore is an exception. Otherwise, if you look at the countries like Indonesia, if you look at countries like Vietnam, they still take a very more security-focused ⁓ approach, especially Vietnam, given their political system, right? So, Meta used to, and I think Meta still have a lot of problems in Vietnam. One of the key issues is about content and moderation, right? There’s a lot of human rights and similar struggles, Thailand too. So those countries, I feel like the sort of the older problems from the social media era was not really solved yet.

And those problems will be brought into the AGI era. And when the government look at the AI, their first question is, okay, so how can I prevent people from using AI to cause any unnecessary trouble, which means like a social instability, right? So that will be the same older problems facing big tech companies. And that tells you a lot when those countries look at AI, they still come from very much a security focused on mindset.

Grace Shao (17:09)

That’s really fascinating because actually when we were just talking about the infrastructure build out on my end, really just I’ve done some research and writing on, you know, the Johor build out and the over capacity with data centers right now. And the unfortunate cause that’s just like the local infrastructure is not able to actually support the the rampant build out. It’s actually affecting the livelihood of people. Right. But your point is really interesting. I didn’t really think about it that way. It’s actually for the from the perspective of these big tech actually.

It’s to prevent bad actors using their technology to actually propel even further, like, you know, bad, intentional, harmful content, right? And then essentially, like you said, cause social unrest that would really be very troublesome for the local government. So I guess from the policy perspective, from the social media era. But what would be something different? What would be something that, you know, big tech will have to start thinking about that they didn’t even have to worry about before.

George Chen (18:03)

Right. Well, you know, as I said, know, lot of the older problems from social media era will remain in the AI era, such as misinformation, know, skin, political speech, you know, especially for countries like Vietnam and Thailand, the real content. It’s always, you know, when I worked at the Meta, you know, those South Eastern countries are always considered as like a high risk countries, you know, when it comes to content policy risks, right?

On the other side, Vietnam, Thailand, Indonesia are much bigger. They are also smart. They also consider AI as opportunity. So they are thinking, how can I use AI to train the next generation of talents, digital talents? Those countries also have relatively younger demographics. So there’s a lot of smart kids who can get on AI and then to learn. So I think that also posed the opportunity for partnership for those American tech companies. Can we do some training program for the purpose to grow the next generation of AI talents in those countries? I think those governments will be very much welcome those initiatives. And this is not just happening in Southeast Asia. You may know somehow I also have my exchange of career experience in Central Asia. I can tell you even countries like Kazakhstan and Uzbekistan trying to focus on talented developments. Because they believe, if you think about learning how to code 10 years ago, this is actually quite an expensive experiment. You need to get professional tutors. You need to get long hours to learn one language. When I grew up, I learned like, I don’t know if you know, we started with Microsoft, the DOS system, and then C12, no one talking about it.

So it took like a year to just get a basic sense of those languages, right? But like AI, you don’t need to learn ⁓ the code. It’s more important for you to understand how to write a proper talk. So those countries like Uzbekistan and Kazakhstan also catch up trying to be the back office for big tech to train, to grow the basic, like the junior engineers. So hopefully they can get some basic work done in those countries for cheap labor cost reasons rather than you need to hire all those engineers in Silicon Valley. And I think that posed the same sort of opportunities for Indonesia, Vietnam, Thailand and other Asian countries.

Grace Shao (20:30)

That’s really interesting. So there’s like a reshuffling of talent and then also like just the talent strategy is actually changing from the social media era or just like the big tech era. I want to kind of look at responsible AI. So we hear the phrase a lot, right? Responsible AI, AI safety from your experience right now.

What does responsible AI actually look like inside of a company and what changes in org charts, KPIs, or decision making when we’re talking about responsible AI? What are the metrics we must track?

George Chen (20:59)

That’s right. Okay, so you mentioned that I wear a lot of hats. You I don’t want to speak like a professor, but I do teach a course at the University of Hong Kong and the Tsinghua University. My course is about digital society and governance. One of the lectures is actually about AI governance for corporates. So responsible AI is a term, you know, very popular, not just in the tech industry, but you now hear more and more just in business in general, right?

It’s, in my view, responsible AI is something like the privacy statement, right? You know, for different companies, you know, when you go to a website now, like the privacy statement already become like the very normal thing, right? You know, when you use a service, you know, have to get, you know, they have to get the user consent first, and they need to tell you, you know, what kind of data they’re collecting for what purpose. That’s the privacy statement. Every website, you will find, you know, a privacy statement. Responsible AI is similar.

So the government is doing their job ⁓ from regulatory perspective, from self regulatory perspective, the government work with NGOs and associations to have an industry code. But for corporates, responsible AI is kind of like the business led principles. I want to use Microsoft as a perfect example. I think Microsoft is leading the way how business can take a more responsible, sustainable approach to AI. Microsoft is responsible for AI. They call it the trustworthy AI, but it’s just the name change, more or less the same. Microsoft very much focused on three pillars, and I believe many other AI type companies focus more or less the same. First is security. You have to have a very secure AI system. That’s the basis. That’s also where the user tries to come from. Second is about safety you talk about online safety, particularly for those more vulnerable groups like children and women, how to address those issues. Again, the same social media problems like harassment, online safety, even suicide prevention, exists, if not get worse. The last one, at least, is privacy. That’s easy to understand. So, safety and security privacy. The three pillars are the key foundations for responsible or trustworthiness or other names. When we talk about the process for big tech or just traditional business like Starbucks, when they want to implement AI in their business, we have a massive means called the privacy by design in the social media era, which means privacy should be the first thing to consider when you develop a product. This is like a rule. ABC like a 101 for any product manager, right? You know, when I worked at the Meta, we always got a reminder like, you know, the engineers, right? It’s not like you have a great product idea and you talk to everyone and finally you think about, okay, I should talk to my privacy lawyer. All right, you should do it the other way around. The first of all, you should talk to is the privacy legal, the privacy team, right? Responsible AI poses a very similar approach. The first thing, when you develop an upgrade or a new service backed by AI, you should think about whether you can tick the three boxes, security, safety, and privacy for the AI services and product you’re going to launch. Microsoft has set a very good example when they’re developing the co-pilot. That’s their AI platform. for you users. So I hope that can give you a very rough sense of what responsible AI is about.

Grace Shao (24:39)

I think what you mentioned just now that stood out to me is that a lot of these big tech companies like Microsoft or Sell, they have very mature, legal, and safety teams in place, right? So it’s much easier for the developers to actually tap into their know-how and their knowledge. And obviously, like you said, an extension of how they use their regular content as well as not just content moderation, but also just product safety. But for startups, I don’t know if you work with them at all or not, but like,

I just the proliferation of AI tools right now, right? It’s like, it’s very, it’s very crazy right now. Basically, like you also kind of hinted at this where like, you know, developing a new product is so much easier than it was say 30 years ago. It’s not only that, like, you know, the language of coding has made it easier, but now we have a jented coding tools, right? So you can have vibe coding, whatnot. How do we actually understand product safety and like responsible AI when we start talking about new products within these startups. And also my question is on a broader like picture, how do we understand responsible AI in a big market like China where a lot of products are consumer facing AI versus maybe the US where it’s a lot more enterprise facing. Can you kind of give us some color on that?

George Chen (25:55)

Right. So first of all, startup, yes, you we do have some startup clients. I’m very glad that the startup clients we work with in the tech sector are very much, you know, either backed by some leading figures in the Central Valley or by global VCs. So I think that they do have, you know, like a stronger internal compliance control. Right. And over the years, I think all the big tech, you from know, met up to Microsoft, you know, to other companies. I think all the classic incidents, know, the lessons, remember, you know, when Mark, when Mark Zuckerberg had to apologize, you know, you know, the Cambridge Analytical incident, right? It feels like a not too far away, you for people who had short memory. I think that those incidents that did, you know, ⁓ serve as very good lessons, you know, for those, you know, I will say like a more US-funded back than startups. I think their goal is clear. If you want to get listed on Nasdaq someday, you’d better do things very right from the beginning. There are some naughty boys, cases from China. You probably noticed there are some AI-cub startups from China.

Like grab the content from Disney, know, Parliament, know, Sony, right, you know, to make those funny, like the AI, you know, effects. But in fact, it was like a serious violation of, you know, IP prototype content, you know, but those start up like, I don’t care. I just like to have fun. Like, let’s see how it goes. then suddenly, you know, they got like a 1 million to 2 million, you know, and then to 10 million users within a week. So, but they’re not going to go far away, right? There’ll be like a long series of this and that. So this is not the right approach. I do think that startups need to be very clear about the boundaries. It’s not like, okay, you are a startup, so you can lower your compliance requirement to do whatever you want. And the end of the day, you need to be responsible, not just responsible AI for the users, you also need to be responsible for your investors, right? So that’s on the on the startup part. In terms of compliance, think the startups in general do pay a lot of attention to compliance with different, especially now AI, as we mentioned, right? If you look at the APAC, there’s no unified approach to AI governance, right? It’s not like EU has AI efforts. So the compliance cost is indeed very high startups. This is the luxury Big Tech have. We just discussed Microsoft as a case study. So Microsoft has pretty decent size of legal team, security team, enforcement team, to support those three pillars, like safety, security, and privacy. But for a startup company, you can imagine they probably only have one legal, one policy manager for everything.

That is it. That is a challenge. And then this is also why a lot of companies complain about very tight regulatory environment in EU because as a startup you don’t want to spend all your money, not even like half of the money as your compliance cost, right? So I always joke with my friends like if you hire more lawyers than engineers for a tech company, I don’t think that’s right. So this is a constant challenge for startup, how to comply, but in the meantime, also keep innovating.

Grace Shao (29:28)

I think that’s really interesting because essentially, like you said, whether it’s a startup or enterprise, in many ways, it’s faced, they’re facing the same issue. But my issue right now with kind of the AI space is actually there’s lack of international standardization, right? So for example, like globally, wherever you go, you can’t really go stab someone. The rules around drugs or even other issues like driving and other safety issues may vary, but there is like a standardized base normalization or what we believe as humans that should not be done, which is essentially don’t kill people. Homicide is illegal anywhere you go, right?

So now with AI, regulation, is that like right now we’re not seeing countries come together and say, this is a sanitized belief that we should just not have. Maybe like you mentioned, child pornography and child safety is something very high on the radar, but even that can be quite subjective from culture to culture. So how do we make of that when we’re going to have AI proliferation across the economy and different touch points in our daily lives?

George Chen (30:33)

That’s a very important, interesting question and point you make. You you reminded me, you know, when I teach my students in the classroom, one of the examples I give them is, you know, I travel a lot, right? So different countries, to different countries, the first question I ask myself, like, which socket do they use for plug, right? And then even in EU, you know, like, well, in the UK, it’s no longer taught on EU.

But even you cross the border sometimes, know, from country to country, you need to, that’s why we always bring a travel adapter, right? So when it comes to AI governance, it’s actually the same problem. You absolutely right in the very spot, EU has the EU AI Act. If you are a startup, you want to expand into EU, no argument, no negotiation, you have to comply with EU AI Act. Plus, several other regulations like the Digital Market Act, the Digital Service Act, then plus GDPR. So to expand into EU is not easy. The compliance cost will be very high. But same thing in APAC. You go to different markets. Indonesia is going to have their own AI regulation versus Singapore, versus other markets. Ideally, the UN should take up a bigger role, a more powerful role to sort of you know, have, you know, control or supervision over like how AI should be used. Right. You know, think about the same question about telephone, you know, when telephone was invented, right? Why, you know, Hong Kong’s, you know, country code is A52, right? Why China is like A6, why US is 001? Because someone made the standard and for telephone code,

That was ITU, the International Telecommunications Union. So some people say we also need someone like ITU. Maybe the UN has an AI panel, but I don’t know how powerful the UN AI panel is. I mean, not to mention that the US government is not really a big fan of UN these days. So I agree, we should have international standards on AI, especially on AI safety as a key part of AI governance. We should have some principles.

So I think this is something all the countries are looking to. We will very soon have the new annual AI summit in India in February in 2026. I think that India also wants to use the AI summit as opportunity to discuss those standard issues. And also to a point, I don’t know how many already realize, actually US and China are not just competing.

In the aspect of AI technologies, like official recognition, deepfake, and other issues, but also compete with each other on AI governance. Basically, the and China are competing, like who’s going to write the rules for AI usage for the next generations. So this is also another flashpoint between the US and China when it comes to technology innovation, not to mention the two countries who are continuing to compete in the aspect of AI technologies, you who’s going to have more faster motors, whether it’s Gemini or DeepSeek in winning the battle. So that would be also a story we watch very closely in terms of competition and struggles.

Grace Shao (33:57)

Yeah, I think it’s also just because the technology is moving so fast right now. It’s really hard for regulators to keep up even domestically in each country at this point. So yeah, I do agree. I think we need some kind of international standardization. I met with Quaishou’s representative a week ago and it was very interesting to hear. They’re very ⁓ focused on the text to image and text to video kind of space. And basically they said in China,

To your point cyber security laws are one of the strictest in the world actually in terms of AI Content AI GC is also one of the strictest in the world She said that actually if you remove the watermark that is actually a criminal offense or like literally you will be like, you know Yeah, it’s quite interesting. And I mean on one hand you think it’s very extreme on the other hand I think it’s very needed right like to make sure that deep fake or the mouth practice or you know, Fabricated content does not spread

George Chen (34:37)

That’s right, yeah.

Grace Shao (34:52)

And kind of lead to the social arrest you mentioned earlier or company disturbance, etc. Or even human to a Harm. Anyway on that note, I want to talk about China. You are interviewed a lot by the media on China US Whether you want to frame as tensions or competition or you know or the race? you know, whatever we want to frame it there is going to be right now two camps essentially, right? ⁓ How do we actually understand the two ecosystems at a high level? Where are the real fault lines? Are they chips, cloud, data, or like you mentioned, regulatory rules? Help us understand the two ecosystems.

George Chen (35:21)

Right. So let’s talk about China. So US and China don’t just compete in technology. US and China also get more more clashes on AI governance in how the way AI should be regulated. US published the AI action plan under the Trump administration. The AI action plan published by the Trump administration is actually already a shift from the AI policy approach taken by President Biden when he was in office. When Biden was in office, it was more like about protection. Biden focused very much on the online safety and this and that. They even set up the US AI Safety Institute. When ⁓ Trump took over, things changed quite differently. Now, Trump is taking American first approach for know, like America’s version of AI innovation, right? Which means like how we can keep American competitive in the aspect of AI technologies. In the meantime, think the Trump administration also want to export the US governance model on AI to the rest of the world, to many of its allies, especially in Asia, you have like Japan, Taiwan, Korea. While China is also trying to influence perhaps mostly global South countries and Bayer Road countries to be more aligned with China’s AI governance ⁓ model. So the two countries are not just competing in technology, but also in the way how AI should be governed.

Grace Shao (37:00)

Think on that note, if you’re a developing country right now, whether you’re in Asia, Africa, Middle East, and you’re listening to pitches from both Washington and Beijing, like you said, essentially they want to capture the rest of the world, what questions should you be asking to avoid being locked into one ecosystem?

George Chen (37:17)

Right, I’m always asked by my friends from global service countries, which side should I take? And my answer is no, you shouldn’t take any side. You should take whatever that fits you to have a sort of combination of the best that you can take from both the US model and also the China model. In some ways, China was quite innovative to solve some unique challenges caused by like a... know, defake and this and that. But in the meantime, the people will say, oh, wow, you know, but you have to sacrifice a lot of, you know, all your privacy, right? You know, even the internet, you you to get on the internet in China, you you must be a real person. know, China has the real ID, you know, policy, right? In Hong Kong, not the same, you you can’t just have, you know, like a, a, a, like a, don’t need to, you know, you have, you know, even for the mobile phone, need to register a number with your real ID. But in the US, this is quite unthinkable. But the real ID approach in Hong Kong and China can certainly strengthen a lot of people’s policy concerns. Versus in the US, everybody can join the party, basically. That will also waste a lot of time. think in China, you will see very much led by companies ⁓ like the traditional BAT and DeepSeek and Huawei to enhance their AI governance through a more company-led plus state-supervised model on AI governance.

Grace Shao (38:48)

I think that’s really interesting. You just mentioned something that struck a chord with me because I was just in Singapore and I was reflecting on how I basically hated my experience there six years ago, seven years ago when I was single without kids because it feels very like, not control, but everything feels very watched and very sterile and you know, your point, everything is very top down. But this time going as a mother of very young children.

George Chen (39:04)

Right.

Grace Shao (39:10)

I loved it. was like, wow, it’s so clean. It’s so safe. I rather give them all my data so they can protect me. They know where to track like ad actors. And I think to your point is very interesting. The idea or the value of Liberty per se may be very different in different cultures and also might change as you go through different phases in your life. So it will be interesting to see how companies or countries choose which ecosystem to join, right? Based on their own.

George Chen (39:17)

Ha ha ha

Grace Shao (39:38)

belief system or value system. I want to ask you, how are the extra controls right now actually affecting companies operating between China and US? Because I know a lot of your clients probably are operating between these two large economies. You sit in Hong Kong and most of your clients, would assume, are actually like MNCs and have some kind of a... You use Hong Kong as some kind of a gateway to intern exit, mainland China, right?

George Chen (39:48)

Mm-hmm.

That’s right. think that the China model so far, the China model is basically a more multi-barad... the more parallelism, right? So like, you know, to work with different countries, more stakeholders approach. This is also what, know, Premier Li Chang, know, when he was in Shanghai, I think in July for the World AI Conference, you he also called AI as a public goods. You know, I find that that concept was quite interesting. Basically, said this is not just something you and I should exclusively hold, right? This is public goods. This is for, you know, like, almost like, you know, this is like for the fate of the whole mankind in the future. So we need to share the success, share in the growth, versus like the American approach is very clear. You know, this is America first, we need to take the lead. And America has always taken the in AI and technology ⁓ innovation. And again, you know, don’t get me wrong. I think both Li Qiang ⁓ as Premier for China and President Trump have their own very good reasons to manage AI in their own ways. One is to keep raising the American flag high and to make yourself a role model, right? The other is to have a more open model everybody can come and share. I think so far the Chinese model perhaps is more appealing to a lot of developing countries, given the-

It’s more like a cost efficiency and a top-down approach also boosts highly efficiency rather than more like a button-up, democratic approach. You need to talk to 10 companies that get alignment, this and that.

Grace Shao (41:39)

Yeah, actually, you just touched on something I was going to ask you. China has pushed up the AI Plus initiative and they were, like you mentioned, Li Qiang and them are just kind of embracing this idea of exporting AI to global south. But beyond the branding, I was going to ask you, do you think it’s actually successful? But it sounds like they are, right? It sounds like the global south is adopting China’s AI ecosystem because it’s more cost efficient, deployable, scalable given that’s open source, open weight, right? I think I want to ask you one last question on this section, is, you comment on China’s AI ecosystem law in the media. What is something that we’re missing here? What are people kind of missing, maybe even in mainstream media that you think is very important for people to know?

George Chen (42:06)

Right. That’s an interesting question. think that the international cooperation part is something I’m quite concerned about. China has a lot of good engineers. Actually, we also saw a lot of engineers coming back to China from the US. However, both sides, the US and China, should talk to each other more to maximize the research capacity for the overall interest of the whole mind can. So far it’s not happening. And then the result is, I also think AI, the reason why AI is so special is I believe AI also touches on ideology, the way how people think about things. So the US right now is very ⁓ US-centric, just focused on their AI. And then China is very much oriental focused, trying to focus on their version. So when the two AI is a basically, you know, a developer like in the parallel approach, you know, you don’t talk to each other and that will result in a more divided world when it comes to content moderation, when it comes to understanding, you know, certain issues, you know, which policy approach you take, you know, to explain a historical event, you know, for example, this, if the two countries, US and China, they don’t talk to each other, it’s not going to be helpful.

For the overall development in R &D. So again, when I teach my course about the YouTube governance, I use the world’s most popular apps as example. Can you believe, it may not be a surprise for you, actually seven out of 10 most popular apps are in English, originally from the US, very much from California. The other three apps are either from China or Singapore, it tells you something. I think when social media companies began to expand into the Asian region, a lot of countries were fearful of the impact that American social media could bring to their markets. They are also talking about the so-called digital colonialism. Which AI you use that will influence your thinking.

So I think in a way, people also need to be mindful whether you are too much into the US model AI, and then that also begin to change the way how you think about things. I actually tested the Chinese AI, and I tried some new features of the AI models. Sorry, I’m trying to think about which model I use. But my point is, the Chinese models are very local, very efficient. For example, when I’m in...

Beijing, right? I’m not going to use Chai Chi Bti, not because of BVPN, but I just find like the DeepSeek, you know, the database they have is more practical and efficient and timely than like say Gemini and Chai Chi Bti 4, right? So if I look for the best noodle restaurant, you know, the DeepSeek in Beijing actually, the answer from DeepSeek, you know, could be much better, more accurate, you know, than Gemini and OpenAI.

Grace Shao (45:17)

That’s really interesting because I think it reminded me of one of the Chinese LLM startups and they said that they’re actually working with local governments in the global south and exactly to your point is that they localize information, localize the culture or language. It goes beyond just the surface language, right? I think that’s really interesting. I wanted to ask you one last question, which is...

What is one differentiated view or non-consensus view you hold? This could be about the AI sphere or it could be about something in life.

George Chen (45:56)

I’m still trying to understand how we should position AI. There is a debate in the industry whether we should position AI as your assistant or more as your partner. I don’t have a clear answer on that. In some cases, I want AI just to be my assistant, which means I tell AI to do what and then you do exactly what I want you to do, right? But...

I also understand if you just position AI as your assistant, that that will also limit the potential of the development capacity of AI. But when you treat AI more as your partner, I’m thinking about one of my favorite movies. I don’t know if you remember, there was a movie called Her. There was an engineer talking to the computer. Scarlett Johansson played the sound part.

Grace Shao (46:41)

Scarlett Johansson, Yeah.

George Chen (46:50)

for the AIs and that was quite a romantic movie, but the ending was not very, it was not a happy ending. So I’m trying to think like if you produce AI as a partner, you can empower AI to do more things, but then, know, whether eventually we will also enter some dangerous territory, you know, to have AI to have, then we need to talk more like ethical issues, right? You treat the AI more as a partner. There were discussions, know, yes, know, AI doesn’t have feeling.

But should we also ask the AI to work for like a nonstop? If you think about human rights, should, if human will work for like 10 hours, 12 hours, you should have to get a break. Why we shouldn’t just keep asking AI all these questions, keep them running the models to get the result. And even AI doesn’t have the touch feeling, does AI have emotional intelligence? I believe at some point that AI will have emotional intelligence. That is to say, if you use AI as sort of a slave.

they will also be unhappy. So back to my point, I don’t have a clear answer, but I’m still wondering which sort of status, like a category, we should put AI into, more as AI assistant or as ⁓ a human car parking.

Grace Shao (47:58)

I think that conversation can like, warrants another conversation on its own, that topic, because I think to your point on the technology aspect, we are seeing a shift from AI just to consumer and just as a chatbot to agent AI, right? So to your point, know, ⁓ AI can actually start completing tasks for you. They can be more proactive, remind you to do things. They are more like a thought partner versus an assistant. But again, to like, you know, to even the conversation we had earlier, it’s like who...

George Chen (48:01)

Ha ha!

Grace Shao (48:26)

Who can play God? Who is to say, where is the line, right? And your 10 to 12 hour work ethic thing is very Chinese and American. Definitely in Europe, people are not working 12 hours a day. That is like a, that is a normal work day for Americans and Chinese. But again, yeah.

George Chen (48:40)

That’s right. You already see the cultural difference here, even for the real world and for the AI in different parts of the world.

Grace Shao (48:46)

Right? So who gets to set the standards? And I think it will become harder and harder. It’s because then it becomes more philosophical and ethical than just, you know, ⁓ practical, which is right now what we’re talking about AI safety is just like, okay, child pornography is fundamentally wrong. Like, homicide videos is not allowed. Don’t create fake videos of people doing fake things. That is very black and white. We can almost just all universally agree.

But when it becomes a cultural, evidently because they’re culture norms, even language norms, societal norms, et cetera, right? Or even each person’s emotional capacity is even different. Then who gets to decide when AI needs to stop, right? That’s definitely like a very interesting topic. And I’ve been having this conversation with friends as well. It’s like, has technology hit a point of actually further development does not progress society as a whole anymore? Or are we still actually benefiting from technological advancements. So anyway, I really, really appreciate that. And I can go on forever. This is an interesting topic. Thank you so much for your time, George. I really, really appreciate your insights and all the expertise and experience you bring to us.

AI Proem is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

In this episode of Differentiated Understanding, I talk with James Wang, general partner at deep-tech fund Creative Ventures, author of What You Need to Know About AI: A Primer on Being Human in an Artificially Intelligent World, and writer of the newsletter Weighty Thoughts. James has sat on nearly every side of the table — Bridgewater investor, startup founder/CTO in healthcare, engineer at Google — and now backs “real-world AI” from semiconductors and interconnects to diagnostics and industrial systems.

We start with how the AI investing landscape has evolved since 2016: why “AI” used to be a dirty word in pitch decks, how the post–ChatGPT boom funneled capital into a small set of model companies, and why so many AI startups shot up to tens of millions in ARR only to fall back as incumbents absorbed their features. James explains where he still sees real opportunity — especially in vertical AI built on hard-to-replicate proprietary data — and why moats in healthcare and industrial AI look very different from the “GPT wrapper” era.

From there, we zoom out. We compare China vs. the US on AI pragmatism, industrial policy, and consumer vs. enterprise strengths; unpack the open-source vs. closed-source model debate; and talk about how agentic AI is already furthest along in developer tools. James also breaks down the energy reality of AI: why GPUs turn power into intelligence, how much additional load AI really adds to the grid, and what the Inflation Reduction Act and its partial rollback actually changed (and didn’t) for data centers and renewables.

We close with James’s differentiated view: that over time, AI’s gains will be largely socialized — diffused into everyday life via cheap, ubiquitous models (often running at the edge) rather than captured as persistent monopoly profits by a tiny set of firms.

In today’s world, there’s no shortage of information. Knowledge is abundant, perspectives are everywhere. But true insight doesn’t come from access alone—it comes from differentiated understanding. It’s the ability to piece together scattered signals, cut through the noise and clutter, and form a clear, original perspective on a situation, a trend, a business, or a person. That’s what makes understanding powerful.

Every episode, I bring in a guest with a unique point of view on a critical matter, phenomenon, or business trend—someone who can help us see things differently.

For more information on the podcast series, see here.

Topics we covered:

* What “real-world AI” means: interconnects, power, semis, diagnostics, industrial systems

* How AI investing has changed from “don’t say AI” to “everyone is an AI startup”

* Why many high-flying AI startups lacked moats and saw revenue fall back to earth

* The case for vertical AI built on scarce, proprietary data (e.g., medical imaging, acoustics)

* China’s strength in industrial AI and consumer apps vs. the US edge in enterprise SaaS

* Open-source vs. closed-source models, and what really matters for enterprise buyers

* What “agentic AI” actually is, and why dev tools are still the most advanced real use case

* AI’s power appetite, data centers going “behind the meter,” and the limits of US grid politics

* Why James believes most of AI’s value will show up in broad productivity gains, not just in a few mega-caps

AI-generated transcipt

Grace Shao (00:01)

Hey everyone, welcome back to another episode of Differential Understanding. Today, joining me is James Wong. James is a general partner at Creative Ventures, spearheading investments in AI across the stack. He was previously the co-founder and CTO of Lioness Health, and before that he was on the core investment team at Bridgewater Associates. He founded a nonprofit consulting firm specializing in microfinance and had a short stint at Google. I’m very excited to actually have you on today, James.

James Wang (00:33)

Super excited to be here too. Thanks so much, Grace.

Grace Shao (00:36)

James, it’s really great to actually finally meet you, I guess, in person. We were just kind of laughing about this. We’ve talked on and off on Substack on WhatsApp, on email for quite a while now. I’ve been a really big fan of your writing and you are actually one of the first paid, I think, subscribers to my own newsletter, AI Proem as well. So for listeners, his newsletter is called Weighty Thoughts. He writes everything about the startup space, VC, AI, know, FinTech, I think, and know, bigger pictures as well, right? But ⁓ start with James, why don’t you tell us about your day job? What is it that you do when you say you invest in AI? What are you investing in? And what kind of businesses are you looking at these days?

James Wang (01:19)

Yeah, sounds great. yeah, glad to be one of the early subscribers to AI Proem because yeah, as a VC, you have to catch the good thing early. it’s a part of the job there. Yeah. So Creative Ventures is an early stage deep tech fund. So deep tech has gone through quite a few evolutions in terms of what it is or isn’t to people.

For us, it is things with harder science and IP barriers. So that includes things like battery manufacturing platforms that cost a billion dollars a piece, AI diagnostics that are completely end to end with no clinicians in the loop, things like materials discovery platforms using AI. So a lot of these different areas that have gotten pretty exciting with AI as well. think one of the interesting things is deep tech, especially within say like some of the materials discovery spaces, the bio space, like a lot of these areas have accelerated quite a bit with AI’s ⁓ involvement at this point. And there’s a lot of exciting things coming up in those areas.

Grace Shao (02:27)

I think you know beyond a business background which a lot of investors have you actually have a technical background as well ⁓ What do you think that like does that make you? More understanding of the deep tech that you’re looking into or do you have any unique perspective on technology companies when you look at investing in them?

James Wang (02:47)

Yeah, totally. So for us, for our team, actually, I’m one of the few folks without a PhD. So a of the team does actually have that kind of background, which is needed within deep tech ⁓ in large part because it’s you do need to understand how the technology works in order to understand the market that it goes into. That being said, like like most technology startups, the ultimate challenge is finding the right market and scaling.

But if you don’t understand the technology on a base level in terms of what it does, it’s really, really hard to actually figure out how to scale the thing. So a lot of that technical background, especially within these areas is quite critical. And I guess just my opinion as well, like a lot of different asset management areas undergo evolution. VC historically has been one that has allowed a lot of generalism within it just because of the nature of how a lot of the software boom went came up and went through and everything. But our opinion is actually a lot of the investors in this particular area will get more and more niche, especially with AI, which I think we might jump into as well. AI does actually involve and help enable a lot of vertically integrated industries ⁓ in interesting ways, which means that, you end up with investors who get more and more specialized in their areas.

Grace Shao (04:00)

Mm-hmm. How big are these ticket sizes that we’re talking about when you’re investing in and how early are you looking at?

James Wang (04:13)

Yeah, typically speaking for us, we are often the first institutional investor in that being said, some of our companies have five, 10, even like 20 million dollars in non dilutive government grant funding or research funding before we actually invest. So it’s kind of hard to say when you’re trying to pin that down. That being said, yeah, we’re among the first investors in. Usually we invest around a million in terms of initial check size and sort of ramp from there.

Grace Shao (04:28)

I see.

James Wang (04:42)

⁓ And then our companies obviously like as they get larger and larger later on They can have quite a bit of range in terms of like where they end up

Grace Shao (04:52)

I see. I definitely want to double click on the vertical AI space later. But to start with, another personal question I want to touch on is your book. You’ve just launched a new book. It came out in October, I believe, right? It’s called What You Need to Know About AI, A Primer on Being Human in an Artificially Intelligent World. Can you tell us a bit about the book, a preview of like, I mean, the gist of your book or why we should go read your book?

James Wang (05:19)

Yeah, totally. Well, think the most recent thing I can remember was someone told me the other day, think yesterday actually, that this is a great stocking stuffer for all the boomers in your life. I believe that was a compliment. So ultimate, I think so. So the book is actually a end to end. Here’s what you need to know to kind of get up to speed on AI.

Grace Shao (05:33)

It sounds like a compliment.

James Wang (05:44)

⁓ It goes through the history of it. It goes through some of the technical background. ⁓ Not too deep, but then again, like also doesn’t really pull too many punches in terms of like actually getting into the structure of it. And then finally it goes into how it’s being used today and some implications of it coming up. So it’s meant to take you through end to end. ⁓ You know, we have a lot of interesting endorsements of it. Reid Hoffman actually had read through it and gave a great endorsement of it as well and I’ve had both people within the AI business sector. So basically people trying to market AI and push it out into market as well as engineers, both tell me that they’ve learned something from it. So I think actually a lot of people can get something out of it. It’s just different people will find different parts of the book difficult or not. but it does attempt to like step you through it. so that was the aim that I had writing the book and, ⁓ hopefully I achieved it so far. sounds like it, if it’s a boomer, baby boomer stocking stuffer.

Grace Shao (06:44)

I think I need to get a book for myself. Does it overlap with what you write about on Weedy Thoughts or is it a bit different?

James Wang (06:52)

It does, but it ⁓ gets into more depth and basically takes you through A to Z a little bit more. Since I’m sure you know as well, it’s like your experience as well there. It’s like for a substack post, inevitably you leave a lot of things out. You kind of hit the high level, you hit the points, but yeah, ⁓ you can’t make the article 10,000 words long or something like that. On the other hand with a book, you do need to actually bring it from beginning to end.

Grace Shao (07:21)

What inspired you to write the book? Because I’m sure you have a busy day job already.

James Wang (07:24)

Yeah, I mean, the first part of it, ⁓ which is the real part of the the marketing story that I give is that which is actually true as well. But the marketing story I give is I sort of looked at the landscape and generally speaking, there’s a lot of great technical resources. There are actually a lot of great sub stack newsletters on AI. A lot of other good places to dive into to get a sense of what’s going on. The problem is in general with the book landscape, a lot of the stuff has been like productivity, get rich quick, et cetera, sort of things within AI, or somewhat more alarmist or very thesis specific driven. It’s like, ⁓ AI is going to do this thing. It’s going to kill us all. It’s going to take all our jobs. It’s going to revolutionize this. Like they’re pushing an agenda. I didn’t really see anything out in the landscape that takes you from A to Z for people who didn’t actually know what was going on and wanted to get up to speed. So I got kind of tired not being able to recommend a book for all the friends who are like, hey, you know what’s going on with the say hi stuff. I don’t know what’s going on with this. I stuff. Where can I go read a single book to find that out? So that was like a big impetus on why I decided to write the book. And in terms of like how I came to it, the publisher actually found me through my sub stack and kept bugging me to write a book. And eventually when I decided to go on this direction, they were like- Are you sure you don’t want to write another thing about how it’s going to kill us all or something? We think that might come off and fly off the shelves a little bit more or like draw people in a little bit more than a light textbook. But nonetheless, it has actually got number one in lot of categories on Amazon for a couple of weeks now. So I think it’s working so far, which I’m happy about.

Grace Shao (09:13)

I think that’s a really pragmatic way of approaching it. like, your point, I think I’ve also noticed in just the AI sphere in general, there’s like kind of the, you know, the investors are talking about money. What’s the return? What’s the return? It’s only about monetary return, right? The tech people, when you speak to them, sometimes frankly, they’re a bit ignorant to the societal implications or they’re not, most of them, let’s just say, people are not evil intrinsically. They just think, okay, tech acceleration is a fundamental goal for them.

And I think sometimes to put the blinders on and they forget about the potential implications of society and the environment on, you know, shifts in like, you know, even dynamics between people and countries. And then like you said, unfortunately, I think the people who really try to bring the awareness and be mindful sometimes can go too extreme. And then what happens is like, let’s reject it. But the reality kind of sits somewhere in between where like you can’t really reject a technology once it’s out of the Pandora box, right?

And then you can’t really only look at the value creation in terms of monetary terms. And you can’t really say, OK, let’s only focus on technology, but not think about all the other consequences. So I think to start our interview, let’s really talk about your philosophical view on this. I think your book really ⁓ resonates with me. What I write about also is trying to help people understand, OK, these are the business implications. This is where the return will be. This is what will be.

This is how the technology change your interactions with each other. But it’s not like you can’t approach it mindfully, right? So I’ll throw it back to you. How do we understand the relationships between if you have to simplify the three kind of caps that we see right now?

James Wang (10:54)

Yeah, I mean, in terms of camps, mean, there’s definitely the I mean, it’s been interesting, right? Because for a while you had did have a group that basically said AI is just a fad. It’s not going to actually do anything. Ultimately, it doesn’t matter. Blah, blah. Not a big deal. You have another group that’s basically like, AI is the AI is going to either kill us or AI is going to take off in terms of singularity. And we’ll have a post. ⁓

We’ll have post-scarcity utopia where everyone will have UBI and AI will do everything for us kind of thing. ⁓ So between those two extremes, I mean, you do also have people who are like, yeah, this is a great business opportunity. We’ll go after it. It has some aspect of all of these things. And like you’re saying, from a societal perspective, I think a lot of the people who boost AI quite a bit, ⁓

Don’t take into account. Yes, there’s going to be disruption. mean, even if you look at the Internet boom, which I think at this point, people have ⁓ misremembered certain aspects of it because it came and went and a lot of our economy has been restructured around it. AI is the same way. It’s going to create disruptions. It’s going to create winners and losers, but it’s going to also help accelerate productivity and do a lot of good in the world, too, like most technologies have ⁓ in the entirety of history.

So it’s a big part of just needing to understand what it can do, what it can’t do, and really where we will see the benefits come out. Because I think otherwise, if you’re just very much on the utopian or doomer perspective, you lose the reality of what ⁓ AI actually is, which is a tool, and what the capabilities of that tool is.

Actually, in terms of this, I think China currently in terms of Chinese AI, which we’ll probably get into and Chinese AI companies have generally had a more pragmatic view of this. ⁓ I’ve had a lot of conversations in Silicon Valley, including with different folks at the model companies, where some of the goal and some of the ultimate aim was, hey, we’re going to get to AGI and then we win. And then it’s all it’s all done and that like, we don’t have to worry about anything else.

I think a lot of that particular mindset viewpoint folly has sort of gone away in the past year or two. But even so, like it tells you something about like the way that a lot of people are looking at this, which is almost pseudo religious.

Grace Shao (13:27)

Yeah, I think definitely to your point, the the caps kind of become bit of like cults themselves as well. It’s even when you cover this space, it’s interesting to meet people who are like all or nothing, very much all or nothing, right? They’re either like, let’s go all in and ignore all the noise and all the issues or go like, let’s reject this. This is just ⁓ inherently evil, which to your point, like all technology disrupts what we know, but

Like you can’t reject it, right? Okay. I think moving on from that, I want to talk about investing in AI. You’ve been investing AI since the early 2020s. I wouldn’t say it’s earliest, but you’ve been in space for longer than most people where, you know, they really jumped in after the chat GPT moment in 2022. So how has that relationship between AI companies and capital change? Like, we’re now hearing a lot of buzzwords. Is this a bubble? Is this like, it’s going to flop? Like, where are you seeing the market kind of like

Where is it at right now? And has that relationship between the founders and the investors changed over the last few years?

James Wang (14:35)

Yeah, it’s an interesting question because yeah, when we had started out, and that was 2016, AI was actually kind of a dirty word just because whenever someone tried to throw AI at something, it was like, yeah, this is kind of scammy. It probably isn’t going to actually work. So you literally had different startup pitches pull AI out of their pitch and basically say, no, no, it’s just statistics. Maybe it’s machine learning, but it’s really just statistics and stuff like that.

So, mean, the way and the evolution of it like changed quite a bit. I think around 2020 was when it started to get a little bit more accepted. And we started to see like certain pitches where it’s like, hey, look, we’re going to do AI for movie studios. And really what we want is to do motion capture for this or something like this. Actually, I think I saw two or three startups around that period trying to do this. And what you really want to do is like, you really want to fund us. We’re going to gather a ton of data.

And then we’re going to train a huge model and then we’re going to win the market. So that was actually kind of a popular thing at that particular point in time, which also ended up becoming unpopular because it ended up not working. but it was post yeah, like some getting some towards the chat, GPT moment that suddenly like everyone knew about AI. took off. A lot of people started putting money into the LLM company, model companies, lot of the companies adjacent to it. And at this point now, I mean, it’s.

Interesting, right? Because I think if you look at headlines, would think anything that has AI in its name instantly gets a ton of funding. But I can tell you just being on the ground, that’s actually not hugely the case. Private markets and VC have still been somewhat more sluggish ⁓ since 2022, since interest rates rose, and since there haven’t been super significant exits across the board, which means a lot of that capital market has been frozen.

If you look at the stats, actually for all of the startup funding and AI funding, a huge proportion of it is actually just the giant model companies having mega round after mega round, or some of the second tier model companies who’ve also had a bunch of mega rounds. We haven’t actually seen like tons of AI company like Dragon, a ton of startup financing. ⁓ Even so, like

AI currently still is the hot thing. So if you’re trying to raise money, especially in Silicon Valley, you generally will probably try to put some sort of AI pitch into your thing, whether or not it actually makes sense or not.

Grace Shao (17:07)

Yeah, I was just gonna say it’s really funny when you said like it used to be a dirty word, whereas now you meet any company, like they could be selling chocolate bars and they’d be like, we’re AI, we’re AI company. They’re really trying to use AI as like the kind of buzzword to hook people in. And it’s interesting to hear from your perspective that actually AI startups are not getting a lot of funding, right? Because in China at least, what I cover in this part of the world,

There’s already the jokes about the last round of AI startups dying out already, like phasing out recently. Yeah, so I don’t know. Is that happening in SF as well?

James Wang (17:45)

Yeah, so it’s interesting. maybe I’ll make the amendment that it’s not AI startups are not getting a lot of funding. AI startups are getting more funding than other types of startups. So one of our companies actually just recently was told that, hey, you’re actually an AI startup, not a health care startup. They’re definitely both. But that was their greatest compliment because that meant that the investor was actually interested in putting money into them. So it’s like it tells you.

Grace Shao (18:12)

That’s so funny.

James Wang (18:14)

Yeah, it tells you something about the landscape.

Grace Shao (18:14)

Yeah.

James Wang (18:15)

⁓ yeah, AI companies get more funding. But yeah, it’s definitely not as much of a bonanza ⁓ as you might think from the outside just by just the numbers thrown at the screen because a lot of the big companies are absorbing. But it is definitely the case that I’ve talked with a couple of other investors who’ve told me about some of the revenue numbers for some of their companies. As much as the revenue numbers shot up, ⁓

Grace Shao (18:27)

Yeah, yeah.

Mm-hmm.

James Wang (18:43)

Let’s see, I’ll obfuscate what this specific company is, but there’s one company that I know of that basically was like shot up to 20, 30, 50 million in ARR in a very short period of time. And the investor who I was talking to was saying, yeah, they’re definitely going to get to 100, 120, whatever it is. The last time I checked in, I think they had dropped back down to 20 and maybe stabilizing down towards 10. So in terms of AI startups dying, the interesting thing is like

A lot of these startups don’t actually have moats. ⁓ Whether or not it’s like some random model company that likely isn’t ever going to get off the ground in terms of having enough compute resources or whatever, or a GPT wrapper, which has become a dirty word or had become a dirty, like derogatory, like phrase to say to some of these companies that just wrap their product around like a chat, GPT API. A lot of these companies don’t have any barriers to entry.

So we’re already seeing them shoot very high up because their products are actually useful. And we can get into that. A lot of these are like programming developer tools, agentic ish tools within developer realm. They go up very quickly. They’re actually quite useful, but then everyone else can utilize, everyone else can build the similar kind of thing very quickly or

Say as Codex came out from ChatGBT or as Cloud Code got better and better and added more capabilities, you have a lot of the big model companies themselves end up just incorporating the functionality that these developer tool AI companies tried to put in, but now they’re obsolete.

Grace Shao (20:24)

Yeah, I think that’s something like I’ve been writing about for the Chinese tech space as well, right? The incumbents end of day have a distribution and what they call the flywheel effect, right? I used to hate the word because I think it sounds really silly, but now I think it really makes sense, right? Like in this case of AI, it’s like if it’s not like we have a new device that we’re interacting with it, so whoever already had existing reach on these operating systems can easily basically just swallow another business, like a newcomer.

and just create a function. And to your point on the coding, the agentic side, I know like Alibaba just came out with ⁓ Codar, who I interviewed a while back. ⁓ ByteDance has their similar tool. Obviously Cursor is still the leader globally right now in terms of capability. ultimately, if one day they all reach a similar, I guess, efficiency or user experience, then if you’re already using Alibaba Cloud and you’re already using their like blah, blah, blah service,

you’re already buying your API there, then why wouldn’t you just use your tool, right? I’m sure it’s the same in the US, like the big players just kind of capture all at this point. ⁓ I want to talk about creative ventures specifically. You guys say you invest in real world AI, right? ⁓ That’s really much like you kind of even touched on healthcare, robotics, manufacturing. It’s maybe less about like the consumer side of things, right? What are exciting businesses you’re seeing and you think are being overlooked right now?

James Wang (21:52)

Yeah, I mean, think two areas. One is there are still a lot of interesting things within. For us, real world AI does actually include things like interconnect companies. It includes power management, storage. It includes like different like semiconductor based companies or semiconductor tool companies. So there’s actually a lot of interesting things going on in that realm. It has been a, for example, with like optical interconnects, optical switches, these other things. That’s been a

place where the semiconductor industry has been interested in going for years. And there’s been roadmaps and industry like things talking about like how we need to go that direction. But ultimately, no one actually ever moved because while we have an existing business, it costs time, it costs money to actually move into that. But the interesting thing with the AI boom has been, OK, all of a sudden there is an impetus to actually start adopting a lot of these technologies with some of the optical interconnects and whatnot.

And there’s been actually some large exits within the space even recently. So that’s an interesting area to us still. And it’s an area that most investors have still shied away from because there’s still been a historical wariness, especially within VC towards hardware, which is ironic since that’s actually where Silicon Valley started in terms of VC. But on the other side, too, there’s a lot of stuff within health care that has been something that we’ve been pretty excited about, at least in the US. Medtech and health care has gone through

Quite a few years now actually of kind of a funding winter where a lot of well-known health care companies, digital health companies just didn’t do very well. A lot of them also tanked on the public market. So it’s just been a super unpopular area for investors. But some of the most interesting and exciting things that I’ve seen within AI have been within the healthcare sector. There’s some that are basically like healthcare productivity optimization.

One of our companies, not to talk up our book too much, but one of our companies is currently the only ⁓ and first and only currently end to end AI diagnostic for them. They’re starting in lung disease, but essentially they’re a diagnostic tool that now you press a button. It sends off the scans. It comes back and gives a diagnostic and gets paid reimbursed by Medicare and all the private insurers. Like there’s no clinician, no technician, nothing in the loop, which actually doesn’t just

like increase margins to software like levels is actually insane in the healthcare sector because there’s just so much red tape. There’s so much red, so many regulatory barriers. There’s so many like pieces that can go wrong and thus you need to check and thus you need to have all these other layers that if a piece of software can actually take all that away and be FDA approved, that’s actually a massive productivity improvement in the space. Again, ours is currently the first and only, but I don’t expect it’s going to stay the only one there’s going to be a lot of really interesting things happening, especially within healthcare and biotech.

Grace Shao (24:51)

It’s interesting because I was just listening to a podcast with a 16 C’s podcast. They’re saying that actually a lot of biotech and med tech companies are routing their trials actually in China, just given that there’s less red tape around a lot of these issues. And I met with a company in Singapore just last week. They are one of the leading AI companies actually using AI to do clinical research and trial a clinical trials. And it’s really interesting. Like you said, AI is advanced enough in some ways that they can actually guarantee there are no issues in these kind of like more tedious work or knowledge work that it doesn’t really need that much human judgment and then the efficiency gain is crazy. So that’s an interesting space. think you’re right. And I think it’s going to blow up not just in the US, but also maybe in China space as well. ⁓ I want to ask you on China, on China AI, open source, closed source, that’s the big topic, right? It’s a whole China’s embracing open source.

The US may be still leading on the closed source models. How do these choices really actually affect the products and the margins ⁓ when you’re looking at these companies? there’s a lot of conversation about their performance, about deployment, but what about when it comes to actual nitty-gritty implementation for the companies? What does it really mean?

James Wang (26:11)

Yeah, I mean, the way that the Chinese ecosystem and the US ecosystem, it’s partially just path dependent. They’ve evolved in very different ways. In the US, you still have a lot of API or subscription usage, like specifically, you know, open AI and anthropic and Google sticking Gemini in essentially every single productivity tool that they own. ⁓ So the way that that market works is you have a lot of paid usage go out there like they wrap, they do GPT wrappers and whatnot. And because China ⁓ was later to some of the, to some of this in terms of like big breakout, essentially some of the open weight, open source stuff helped ⁓ spur adoption. So for some of the people who do not want to simply pay for chat, GBT or Anthropics API and just wrap their stuff around it and want to control some of their own destiny.

It’s great to have something like Quen that you can basically fine tune. can locally host. You can locally host DeepSeq. You might not choose to. Ultimately, you might go to a number of different providers who all allow you to have it available there. But nonetheless, you basically have an easier way of saying that you won’t have lock-in. You’ll be able to use this. You’ll be able to go out there and...

Uh, go out there and integrate it. So it is a, again, a little bit of path dependency there. It’s a little bit catching up in, from that perspective as a whole, ultimately in the longterm, I do think like some of the open weight stuff probably does make sense for the same reason. Open source made sense within a lot of the software ecosystem. It does actually spur a lot of enterprise uptake. can pay for support and other things around it. And the bigger thing will ultimately be, can you sell it faster? One of the things about SaaS, so software as a service, has always been, there’s no real barrier to entry for you switching to someone else, except for the fact that I made, especially for enterprise SaaS, an enterprise decision, and I don’t want to switch away from your product now there’s nothing really at the end of the day that makes Salesforce versus some other CRM versus some other productivity tool that different from one another. And at the end of the day, for a lot of the LLMs, especially as we start hitting plateaus in noticeable performance improvement for people. They might become quite interchangeable in which case it becomes similar to a SaaS decision. Do I want to choose the closed source one where

I will have to pay for it forever and also potentially have it go down and only have a single source vendor. Or am I going to take the open weight, open source version where maybe I’ll still pay for it to be hosted, but I can always be comforted that I can always like take it, roll it, and put it in my own infrastructure as well.

Grace Shao (29:15)

Yeah, I think right now where we’re at as the models, their own performance are getting closer and closer and like the gap is not as wide anymore. I see your point. It becomes like an infrastructure. And then I guess for enterprises, biggest issue or the hurdle is really the switching cost of like the bureaucratic switching costs. It’s like going through the legal work internally, making sure all your departments are upgraded the same way that that becomes a switching cost. So then

I guess incumbents still have the advantage once they’re like chosen as the default provider, they get to kind of own that space, right? ⁓ I want to talk about agents. You kind of mentioned earlier, like enterprises like Google are essentially plugging in Gemini into every single productivity tool. And right now we’re hearing a lot about agentic AI, how that’s going to even increase the capabilities of these productivity tools even further. How do we understand

James Wang (29:53)

Yeah.

Grace Shao (30:13)

what even is a gendered AI right now. I think a lot of people still think of AI as just chat GPT chatbots.

James Wang (30:20)

Yeah, I mean, agentic AI became a big buzzword. personally, my personal take, I’ll define it a second, but my personal take is eventually agentic as a term will go away. And we’re just going to say AI because all the big model companies are also going that direction in being agentic. Now, agentic, what does it mean? Well, definitions vary, but at the end of the day, it’s, it doesn’t just chat with you.

It goes and does something. So instead of I plug into chat, GPT say, Hey, where would be a good place to go on vacation? I would tell the agent, okay, I want to go on vacation. helps me research, but then it also helps me book the tickets, the hotel and like give me like the roadmap and plan and stuff like that and do the things for me. So it’s that layer of being able to interact with the world, whether it’s like true real world or whether it’s like digital world in terms of booking stuff, it can actually do things for you. Now, why, why I say it’s eventually just all going to be agentic, in which case we’re stopped going to stop saying agentic. It’s because the direction of where the model companies have gone is this direction. Uh, as some of the performance differences have disappeared, they’ve implemented more and more agentic tools. would say the most advanced agentic area, even though we don’t usually term it that way, is a lot of the developer tools. Ultimately, at the end of the day, for all the developer tools, they will take your input and they will make changes on your machine, on your code, and commit it. So at the end of the day, it’s doing things.

James Wang (32:19)

So at the end of the day, in terms of these agentic tools, where developer tools have been the most advanced because they actually go out there and make changes on your machine, your code, commit it, ⁓ all the different model companies have been rolling out more and more tools that specifically are helping you do things in the real world. As you stop having as much difference in how well they chat with you, there’s going to need to be other differentiation. And the place where they’re going, where there’s lower hanging fruit, is being able to actually implement and do things for you. that’s why I think agentic is interesting. Agentic is actually going to be big, but it’s just not that interesting of a term because ultimately most all the AI companies will likely go into it and implement it with their models.

Grace Shao (33:07)

You’re right, because I think even just this week Deep announced a new like v3 too and they’re saying oh our capabilities are really focused on a genetic AI and every single model is kind of coming out say the same thing and then this is a bit random but it reminded me of this like funny thing was growing up my friend who’s German descent one day asked me she said grace in your household do you guys say eating Chinese food Chinese food do you call Chinese food Chinese food I said no we just say we’re gonna eat food tonight you know and because he normalized and that’s default thing then you wouldn’t really like actually add these prefaced ⁓ adjectives, right? So I get your point about that. ⁓ What kind of agentic products are actually good right now, actually on this point? While models are all becoming more agentic, what actually is it being used in right now? And what tools are actually proving themselves to be really capable and productivity and enforcing?

James Wang (34:03)

Yeah, I mean, I’ve seen various attempts to do things like ⁓ shopping aids, ⁓ different things with, yes, travel booking, concierge services, ⁓ email responses, bot, chat bot, like customer service ticket, chat bot kind of things. I would still say like among these different things, the most advanced is actually still the developer tools ⁓ ultimately.

Grace Shao (34:07)

Mm.

James Wang (34:30)

⁓ it’s close to the companies. It’s close to the people creating it. It’s right now the most advanced area that I see. ⁓ but there’s been a lot of experiments in many of these different areas and they are starting to get better and better and work better and better. So I do actually expect for a lot of these different things where, especially where I guess the framework that I would put on it is if the agentic application is low enough stakes from the perspective of there is a human in the loop that will check it at some point, like, hey, I’m going to book your vacation. Here’s all the pieces of booking your vacation. And you look at it and go, yes, you are correct. I am going to Athens, Greece, not Athens, Georgia, and something like that. If there’s a human in the loop and something where there’s a check, ⁓ these are actually applications that the AI can do quite well and is actually something that’s very implementable currently. So that’s kind of the layer that I put on it. But yeah, like some of these areas are getting quite advanced.

Grace Shao (35:32)

Yeah, like you said, you need to have that human verification. It reminded me of that new show. It was like a silly rom-com. It’s like they bring these women to Paris, but it’s actually like Paris, Texas, and everyone get off the flight and they’re like, my God, this is not the Paris I imagined. Okay, I wanna kinda go into China a little bit. Like I said, you were one of the first paid subscribers to my newsletter and I very much focus on the China space, although I do cover a bit of APAC and different companies as well. What piqued your interest in China AI? Because it’s not like you actually directly invest in China AI, right? So tell us a bit about that.

James Wang (36:11)

Yeah, we’re not able to for various ⁓ investment restrictions, some of our investors and whatnot. But at the end of the day, ⁓ it’s a global market. China is a huge market and China also has a lot of talent within it. It’s kind of funny because it’s like, why was I interested, for example, with your newsletter? ⁓ One, you write well, so there’s that. But also it’s just like having the perspective within the market is super important.

So I actually still have a lot of conversations with Chinese companies. They know I can’t invest, but they’re always kind of interested in also learning about what’s happening in Silicon Valley, et cetera. So I keep a pulse on the Chinese market that way. And that helps inform my decisions, but also my understanding of like, what does the landscape look like in the U S it’s both compare and contrast, but it’s also thinking longer term. What’s going to happen as all of these companies go more and more global.

whether or not they’re competing directly in China or the US, you’re going to encounter each other in the rest of the world, right? So there’s a question in terms of that. As for why, there’s also not very many good sources on China. I think you covered this in some of your articles about some of your own personal history, Grace, but it’s just like, even recently when I was trying to prepare a presentation for talking to some folks about some of the things happening in China where AI is being pushed.

especially by the state into a lot of areas like insurance and whatnot. I was trying to Google like what’s going on in China with that, like with just like very simple terms. And really what came up for me was like New York Times articles about Chinese surveillance state is China like doing these things to the Uyghurs is like what it’s like all of these different things that were obviously had its own like bias, let’s say. ⁓ And at the end of the day, it’s like China.

Grace Shao (37:36)

Hmm.

James Wang (38:02)

especially for the West has been a little bit of a cipher. It’s either can’t innovate at all and only copies things, or it’s like the crazy, huge country that suddenly like will be able to overtake everyone and like whatever it is. It’s the country that who’s the state dictates everything and thus has like total control and everything. At the same time, it’s like, it’s like a super like, like, you know, lot of the private sector stuff does things, but also like the states can’t seem to like do anything right. And then there’s corruption and ⁓ rockets, rocket fuel being used in hot pot or whatever. And the reality is it’s like, it’s all of these things together. Right. But a lot of the way that the media portrays it is fairly biased in terms of that. So it’s always very useful. And I’d say critical for investors in any part of the world to have a pulse on definitely the two biggest economies in the world. Right.

Grace Shao (38:58)

Yeah, I think I can go on forever about the media coverage. I wouldn’t even say it’s biased. I would just say the media business model itself actually awards, you know, clicks and attention and in this time and age and what gets attention, the joke amongst a lot of like I expect journalists in the APAC region, it would say it’s big China, bad China, weird China. So I was like, my God, it’s so many people. It’s so big, weird kind of, my god, they eat dogs, which like honestly, majority of people don’t, but sure, I’m sure some certain small segments of people do have weird diets, right? And then it’s like, like bad, bad, bad, like it’s so bad. So I think that gets the clicks, but I do get your point, you know, not to kind of bring it back to myself too much, but it really is why I started writing about it. was like, there should be a nuance understanding what’s happening, especially in the business space when it’s sometimes not really relevant to what we just talked about, the big, bad and weird. It’s just innovation and business. So I want to bring it back to that. lot of people are talking about China being very, very strong and in industrial planning, right? I think this is something that’s all of a sudden for some reason, making headlines all over the U S and last like three months, whereas like industrial planning didn’t come out three months ago. They they’ve been around for the last 30 years, really.

From your investor lens, where do you see China moving the fastest? it like only sectors within industrial policy support, like the EVs and the hardware and the robotics? Or do you think China actually has its own mayor and certain sectors are being overlooked? And in kind of comparing that to where you’re seeing the US in terms of the companies you’re investing in, what are things you can actually learn from? I think we can talk about that a bit.

James Wang (40:44)

Yeah, I mean, in terms of China, there’s definitely a strong advantage in some of those physical industrial areas, ⁓ including say industrial AI, because it doesn’t exist in the US. The US doesn’t really build that many things anymore. It’s really hard to actually get any sort of manufacturing up. I can tell stories in terms of lioness. I can tell stories in terms of some of the med tech companies have helped try to like figure out where to do manufacturing. Essentially, you can’t do it in the US.

So all of those sectors and areas ultimately do end up being a very strong advantage for China because it exists there and it doesn’t exist here. In terms of like other things that are overlooked, mean, China and actually Asia in general has had an interesting brilliance within consumer, like the consumer sort of trends there, the apps, the like different ways that, yeah, sure. Like WeChat and other things like.

Overall, like China has a much more interesting grasp of like some of the consumer landscape than the US has. I think the, the, I wouldn’t say it’s the, it is, it’s like the stereotype is essentially US companies are the ones that do enterprise SaaS. And the other side of it, which isn’t really spoken at least around here is actually China and actually a lot of Asia is really good at consumer.

A larger consumer market, maybe you can argue it’s like some aspects of that, but maybe you can argue it’s some aspect of taste as well. That may be changing ⁓ over time, especially as like the two markets are more and more divorced from each other. ⁓ Ultimately, the U.S. will probably have its own like consumer ecosystem because it’s divorced away from some of the Asian companies in China in particular. China will get separated from like the enterprise sass in the U.S., in which case there has to be its own stack.

So maybe that will change over time, but there definitely is like sort of a strong, I wouldn’t say internal cult. I wouldn’t say like cultural from a cultural perspective, but more like cultural from there have been entrepreneurs, successful tech companies and sort of playbooks on like, how do you do this? That are much more mature, say in like the Chinese ecosystem than in the U S ecosystem. Well, you know, the last big consumer app was Snapchat and before that, you know, Instagram and Facebook rewinding to the dinosaurs.

Grace Shao (43:06)

Yeah, I think that’s interesting. And I think I’ve been hearing more from founders in AIPAC, not just China, but they’re saying with AI, they actually think there’ll be more opportunities in the enterprise space. And the reasoning is because a lot of the reasons why China or South Korea, a lot of these countries at that time, I would say in the 90s, did not want to adopt a lot of SaaS from the US was actually because they frankly didn’t even have the infrastructure in place as in you know internet was not that like you know, you took What’s the word for it ubiquitous and then it was like they don’t have the ability to actually even Serve the you know the need and then on top of that It’s not just China that has a lot of SOEs actually a lot of Asian countries have a lot of state majority companies and I think people again in the West might not realize that

And because of that, there’s actually a lot of concern on data privacy issues. And they’d rather have maybe even a shittier quality product than actually jeopardize their data being ⁓ taken by someone else, a third party. So that also goes back to why a lot of companies now in Asia actually want to adopt open source AI versus sending their data across the world. Not quite literally, but giving that re-access to like say a chat jpg or a google gemini so i think there’s a lot of reasons why people might find more use cases of sas ai in asia now ⁓ and they might actually you might see more entrepreneurs in this space popping up ⁓ i think on that i want to ask a question on vertical ai i think we kind of touched on healthcare right now we’re talking about there’s a potential growth in enterprise softwares are ai empowered

How do we understand vertical AI? Will they still basically have to rely on the incumbents ⁓ infrastructure to build their tools? should we actually kind of see them grow out? should they be compared to the Googles and the Microsofts of the world? Or should they have their own kind of racetrack themselves?

James Wang (45:19)

I think it’s more their own racetrack because it’s kind of a very different ecosystem based on how AI is evolving. So I think the first layer to talk about is just do any of the LLM model companies have a strong, durable advantage outside of, know, OpenAI and chat GBT has a big brand. Google has a lot of distribution. Alibaba has a lot of distribution. It’s like, is their durable advantage? All of their compute tends to be the same.

You know, they’re using the same GPUs. They’re mostly still on CUDA. Maybe it’s moving a little bit, but they’re mostly still on Nvidia GPUs. The models are basically the same. They’re all transformer based models, essentially. And their data at the end of the day, scraped from the internet, is largely the same. Yeah, maybe it varies a little bit, but it’s largely the same. They’re ultimately going to be very, very similar. That just doesn’t give you a lot of differentiation across that. that goes towards like, well, ultimately it might be capped in terms of how different these things are.

That goes into the difference between that and the internet, right? The internet was very much an aggregated landscape where it’s like everything’s on the internet. You can go find, like go through to Google, whatever, find whatever you’re looking for. You can go to centralized marketplaces, you know, how about like Amazon, whatever you can find your things. And ultimately like it very much like put people into the same place.

What AI is doing, interestingly, is if you think about where there’s actual durable advantage, it’s probably still the computers largely going to be the same. The models in terms of deep learning models, whether they’re transformers or not, are probably also going to be fairly similar. The difference is going to be in the proprietary data. And once you actually get down to the proprietary data level, we’re not just talking about, within your individual corporation, you have your OK.

Internal documents or whatever fine. That’s like one thing Maybe you can still use like chat GBT or like one or whatever like some sort of open source LLM But what if you have raw acoustic data from ultrasounds to be able to detect liver disease? You’re not gonna be able to put that into chat GBT at the same time like that data Right now with the current models and compute you don’t actually need that sophisticated of a model

or even that much compute to make it actually do something really interesting with where AI has advanced to at this point. The same has happened with a lot of the drug discovery area, material science area, industrial AI, which again, China has an advantage in terms of this with their actual like running operations and data gathering exercises there. But that’s where the vertical AI comes into play. If you essentially have the ability to have this proprietary data that’s very expensive and difficult to generate,

Grace Shao (47:40)

Yeah.

James Wang (48:09)

that you have a great proprietary source for, you can build a durable advantage with your specific models there. So that’s where we’re seeing a lot of split in this area. And the way that this will work is less like aggregation, like the horizontal aggregation of search engines or marketplaces. Instead, what you have is a lot of vertical productivity, where you can build.

Like say for drug discovery, can very easily imagine where if you can make a huge amount of productivity increase in drug discovery, that is a massive market, even if you stay just there forever.

Grace Shao (48:46)

Yeah, and it’s not like these big tech incumbents are going to move into that space. Frankly, it’s not it’s not just like your point. It’s not even just having the money to buy these data. It’s also having the know-how and the like decades of, you know, data gathering that you had to collect. And you can’t really get that immediately right now, right? From the market, from just like the public market or anything. It’s very different from the data you’re scraping from the Internet. Yeah.

James Wang (49:09)

They also do have their own flywheels that prevent followership. So the flywheel is specifically this. You get the data when it’s cheap because no one thinks it’s valuable. We have one company that literally did this in terms of raw acoustic data. No one usually keeps raw ultrasound data. You usually just have the images, if that, that is kept. No one keeps the raw ultrasound. They bought a lot of it.

Afterwards, in terms of other AI companies that come, they go, ⁓ actually, this stuff is super useful. We didn’t realize that they also approach hospitals and say, let’s buy this. The hospitals wise up, right? They’re like, ⁓ this is actually useful. We’re not going to sell you it for next to nothing. We’re going to sell it to you for a lot of money. In the meantime, the other AI company has been using this data to generate revenue, to do these things, to get to a certain level of quality, such that now it’s like, the gap is getting bigger and bigger. The data is getting more and more expensive because it’s becoming like clear that it’s valuable. At the same time, the level of productivity, the level of quality of your AI is likely going to lag the incumbent that already gathered a bunch of data and is generating data at the same time and continuing to improve the model. So they have their own flywheel effects with these as well.

Grace Shao (50:24)

I see, see. And I think, okay, I don’t know if this is too small of a use case, but I can already imagine, like I just had a baby, right? And then, you know, we went through the private clinic kind of system in Hong Kong where you basically get, they know you have corporate insurance, so they just like make you pay it a ton. And you go in every month, which is absolutely ridiculous. But at same time, when you are a mother, you want to check in and see if there’s anything that needs to be, you know, there’s any flaring issues. Whereas I know in the public sector, if you go to the public system,

You don’t get like ultrasounds until you’re what, like three months into your pregnancy, 12 weeks in. You don’t get checked very regularly. It’s usually every trimester maybe once or twice max. ⁓ So yeah, like for something that’s not simple, but as predictable as like a healthy pregnancy, I can imagine if you have an AI telling the mom and you just pay like 20 bucks a month, like everything looks normal, everything looks fine.

I can totally imagine that being quite useful for the general mass. if they flare up anything like glaring, that’s something that needs more attention, you can then go into a medical staff’s office for proper checkup.

James Wang (51:27)

Totally.

Right. Well, for one, congratulations. But for two, there’s all there’s so think about it. So one of our companies actually has a product like this, not for the consumer. But ⁓ I’m sure after the baby was born, the doctor did a child hip dysplasia check. So basically checking whether or not the child has this specific condition where if you catch it early, all you need to do is it’s

Grace Shao (51:41)

Thank you.

Mm-hmm.

James Wang (52:07)

not all you need to do. It’s kind of annoying. You do have to put braces on the child and everything, but it fixes through a few months the problem for the child’s entire life. So it’s very worth it versus having a condition the child’s entire life. One of our companies, ultrasound company has an AI. Basically their end to end product is to do a quick sweep with the ultrasound where it tells you hip dysplasia. Yes, no. So in terms of that,

Grace Shao (52:29)

Mm-hmm.

Yeah, actually, my baby, we had to go through for that and you have to go to like you have to wait like two weeks for the specialist to check you and the specialist you have to wait hours in a clinic like it’s a long tedious process and it’s expensive. So I can imagine this just being lot more affordable and you can actually deploy it to the mass like across like mass market a lot faster.

James Wang (52:46)

Yes. Well, even with that, it’s just like, again, taking it from like a, I don’t know, evil commercial VC hat, but not really. It’s just like, think about it from a hospital’s perspective. That check is really easy to miss. Like you have checklists, you have all of these things, but you can forget quite easily. That is a huge impact if you forget and it isn’t caught, right? At the same time, it’s like, it’s a doctor that needs to go do the thing. It’s a very valuable resource that needs to stop get sectioned out to go do the thing. If you are a hospital and you are able to basically just have a nurse do a quick sweep and scan and tells you yes, no, and you’ve checked it off the box, you’re probably willing to actually pay a lot for that at the end of the day. And it actually is more beneficial for the consumer too because it is actually doing the thing, super important. It gets done. It’s very accurate.

A lot of the vertical AI areas are this way. like, it’s not just productivity increases from the, it’s not just like cost decreases maybe is how I see it. It’s not just the cost goes down. It’s just that the quality of it, the productivity of it, the like accessibility that goes up. And if anything, a lot of cases, the hospitals that whatever the vertical AI case is perfectly happy to actually pay up to their cost, previous cost of the thing.

because it’s just so much more reliable, easier, and takes away other workflow concerns. So that’s why a lot of this vertical AI stuff is interesting. Its individual price tends to be actually higher, which is not what you typically think with AI, but it’s just more accurate, easier, smoother, and better for the workflow.

Grace Shao (54:25)

Yeah. I can see that. Yeah, that’s really interesting. I want to talk a little bit about the big picture policies between China and the US right now. I know you invest across the stack, including some of the infra stuff. When we talked about earlier, you said, look at data centers as well, So help us understand this. Data centers aren’t new.

Like, you know, AI needs a lot of energy. AI needs a lot of data centers. How do we understand the relationship between these moving pieces?

James Wang (55:11)

Yeah, mean, the big thing is AI data centers tend to take a lot more power ⁓ in general for lot of the internet services and other things. ⁓ Because if you’re using GPUs inference, these tend to be much more power hungry components. For internet services, part of the reason why SAS could basically make money with queries that are fractions, fractions of ascent.

is because essentially it’s almost free. Like you can use a lot of the way that the internet worked is much more around uptime. So if you actually have, and this may get a little bit technical, if you say have like AWS cloud provider share resources, you’re able to surge up and down your capacity and share it across in terms of virtual instances. actual cost of service, a lot of websites, even massive ones is actually not super high. It’s only high from the perspective of like it may be millions of dollars.

But then again, you’re making billions of dollars off of your service that you’re servicing it from. It’s actually not very high. For AI in general, ⁓ its inference costs have been dropping a lot. But even so, with larger models, with needing a ton of memory, with needing a ton of these different things, with GPUs that themselves are both power hungry, but also heat, generate a lot of heat, you basically need to spend the currency of AI is essentially power.

Grace Shao (56:21)

Mm-hmm.

James Wang (56:38)

You need to spend power to literally power the GPUs or whatever XPUs, like TPUs, whatever thing you’re doing to run the AI. And you also need to cool it, which also is generally active, which means it’s also power. So all of it boils down to, okay, we need to spend power to be able to do this thing. It’s the closest thing to it is actually like cryptocurrencies in terms of you actually think of the one-to-one translation between power and actually the thing, ⁓ like what the thing does. So.

Because of that, the sheer density of power requirements means that usually some of these data centers that are trying to serve AI might exceed the power able to be provided from a local grid that was otherwise serving, just like city, resident, and like normal kind of activity. And you are seeing a lot of these data centers for that reason basically doing their own power purchase agreements.

having their own power plants. So they’re not actually on the grid, but they’re basically connected to their own power plants or connected to some of these power systems that are not within like say residential grids or something like that. So that’s been a big part of like why AI has needed that.

Grace Shao (57:38)

Mm-hmm.

As like a average user of AI, should that mean that we should just be more mindful and not use so much AI? Or does that mean that the future of energy consumption will drop as technology advances? Like, how do we understand that? Because like, you know, when we use the internet, it’s not like we think about, my God, how much power consuming, right?

James Wang (58:11)

Yeah. And the thing that I said before was if you take various stats, it’s somewhere between like 70 to 90 % decrease in inference cost each, like each year. So why haven’t like, you know, inference costs falling through the floor while we’re getting more advanced models, we get reasoning models, which actually use way more tokens or words in order to spit out like the same number of tokens that you see.

Grace Shao (58:36)

Yeah.

James Wang (58:38)

So we’re using more and more and more. And that’s why, even though the cost has been dropping so rapidly, we’ve basically kept pace or exceeded it in terms of power. That being said, there’s a question. Where will some of that power requirement ultimately go? How much will be needed? And yeah, will it be the case that we end up just needing exponentially more power? So there’s actually a piece on my sub stack that a

a hedge fund buddy of mine, hedge fund friend of mine from Bridgewater wrote, he does a commodity hedge fund now. His point is actually, even if you take very aggressive estimates as for how much power needs will grow for AI, it’s around like a 3.5 % incremental. That 3.5 % is basically the growth rate that we had during the 1950s in terms of the US power grid growing.

That can pretty easily be hit by renewables, which have intermittency problems. So you basically need battery storage, which is why we also invest in stationary batteries in that area. Or it can be hit by natural gas, or it can even be hit by just retiring coal plants slower. So actually, a lot of the power needs are not as insurmountable as you might think. And I personally suspect it will ultimately be the case that as we plateau in terms of, hey, this thing like

We don’t need it to like give us like, has much reasoning anymore. We just needed to book us vacation tickets or something like that. That’ll ultimately level off while the requirements in terms of compute costs, in terms of power costs will keep falling too.

Grace Shao (1:00:14)

I see, I see. And I think it’s also interesting, so just spoke to David Fishman recently. He’s an energy expert on the China space. And he was saying that, like he kind of mentioned in passing the US side, which is like essentially the US energy kind of, I guess conundrum is more exacerbated because

the center of living has not increased drastically. So people’s consumption of energy have not actually increased drastically. Whereas in China, over the last two decades, energy consumption has been increasing anyway because of urbanization, because of modernization of maybe your home, the economy as a whole. So there’s been more energy planning in China to actually support that kind of energy increasing demand.

And when that AI is now part of the picture, it doesn’t feel like a sudden gap that needs to be filled because you have the renewables, you have small nuclear plants being built out, et cetera. So it’s interesting to hear your perspective that actually the increase in demand, the increasing energy demand is actually not that significant. I think, again, headlines of news articles often really highlight that and really showcase a different picture where sometimes it’s more about like, OK.

People are experiencing higher utility bills. The grid cannot actually support local economies or local people’s livelihood anymore. It seems like it’s causing a big issue for the average citizen. ⁓ But yeah, thank you for putting that into perspective.

James Wang (1:01:42)

It’s totally, well, it’s a self-inflicted issue on the US side. Again, like the US has expanded faster than that at periods in its history. There’s a lot of different energy sources that you can actually use to go after that. It’s just the problem is political in part, like the US has a lot of bureaucracy red tape that’s hard to cut through, in which case it’s hard to build anything economically in the US, which is part of the problem. There’s no nuclear being built.

Grace Shao (1:01:47)

Mm-hmm.

James Wang (1:02:09)

So like you’re saying, China is actually building nuclear at a pretty rapid clip. The US is at best unretiring or maybe retiring slowly its existing nuclear capacity. It’s actively retiring its coal capacity, whereas China is what building a new coal. I think it’s one or two new coal plants every week or something like that in terms of the pace. like it’s just a very different kind of environment.

But it’s also not because yeah, the U S has no technological ability to go after that. It’s yes. Like you’re saying it’s like we have plateaued and a lot of our energy use. There’s also been a big push towards green renewable energy sources, which especially with the U S grid, low power storage, ⁓ it has its own challenges and can’t actually do the base load for AI. So if we wanted to, the U S could actually pretty quickly solve its problem. The question is, is there the political will and is there the willingness to stomach some of the trade offs for sake?

James Wang (1:03:09)

higher carbon cost.

Grace Shao (1:03:11)

Yeah, and I think that’s something David talked about as well in that episode ⁓ where it’s like the trade-off in China is more like, okay, we need more energy so we build more coal, but it doesn’t mean that we stop our renewable. But just because we have renewable doesn’t mean that we stop our coal. The trade-off obviously can be criticized, know, environmental issues, pollution, et cetera. But again, it’s just state level, I guess, mandate or state level priorities a bit different. ⁓

So we’re not a political show. We’re going to move on from that. I want to ask you about the Inflation Reduction Act. So this relates to what you just talked about, a lot of the push on renewable energy. then Trump kind of taking it 180 degree on this. So the IRA was introduced in 2022. It tried to make solar and wind more affordable on the grid. How did that actually work out?

What does it mean now with the Trump’s one big beautiful bill? Give us a high level explanation what’s happening there.

James Wang (1:04:09)

⁓ let’s see, data center developers keep getting whiplash in terms of renewables being good and then bad and then maybe not so bad, but not good either. Something like that. I think that’s sort of the quick high level. I mean, so, ⁓ a lot of the incentives, ⁓ were definitely something, things that a lot of data centers, lot of other folks, like hyperscalers tried to take advantage of, ⁓ when the inflation reduction act was more the law of the land before, you know,

Some of that got thrown out, big, beautiful bill, et cetera. ⁓ But I mean, the big challenge for the US, though, even just stepping back from that, is regardless of how much legislation you throw at it, it’s just like the CHIPS Act, right? You can throw as much legislation at the CHIPS Act to say, we’re suddenly going to build all our chips in the US now, or something like that. And it’s like, well, ⁓ you’re not spending enough money to do that.

And also legislation doesn’t like magically change things unless it specifically hit some of the core problems, which is yeah, the US doesn’t have for chips is like the US doesn’t have enough like labor for this like expertise moved over for sure. It’s ever for the power side. The problem actually goes back to the same thing we just talked about. Transmission interconnects lines old, hard to do, lots of red tape, lots of bureaucracy. It’s hard to build much anywhere.

unless you’re building in places that might not actually be super optimal for say like data centers. So, you know, some of the South in terms of Texas or Southwest has been more amenable to some of the data center and like power build out. It’s also hot there. It would really be nice to put it in a colder place. So you have less power needs to cool the thing too. ⁓ The bigger story, I think with all of this, there’s been a lot of legislation that the US keeps throwing out.

Grace Shao (1:05:54)

Yeah.

James Wang (1:06:00)

Maybe the bigger story I’d say is just the legislation has done some things around the margins. It has not made like a huge 80 20 change, at least from what I’ve seen. It’s like the same problems, the same ultimate macro problems that plague the U.S. and building stuff. And also it’s aging power grids and interconnect problems between different grids are still the same ones, like regardless of the legislative regime that we’re in.

Grace Shao (1:06:24)

Yeah.

An agent issue with the grid is actually like also just a reflection of like, frankly, the US developed and modernized so much earlier than China. And the grid just by nature is older and therefore the capacity and capability is like weaker because technology advance. Right. I think sometimes people forget about that. Just the reality that China didn’t become China that we know of today until like this decade. And the US has been basically the US that we know of today. The last four decades. Right. ⁓

Grace Shao (1:07:33)

Then I have one last question for you. I have one last question for you and it’s a question I ask every single guest that comes on the show, which is what is one differentiated view you have? Our show is called Differentiated Understanding. It’s about how you piece together the information you have and how you form a differentiated view, right? So what is something that you think is a bit non-consensus or against what the majority might think?

James Wang (1:07:35)

Sounds good. Yeah, I mean, I probably would have said it was my view about the vertical AI thing before, because I was talking about that a lot earlier than a lot of other folks, when there was still the talk about foundational models, which still is somewhat talked about. People are really pushing that a little bit less, that foundational models will cover every single use case in existence. And I think there’s been a lot more consensus moved towards that. So maybe

That was a very non-consensus view I had. The consensus has moved more towards. Let’s see, is there any other big non-consensus view right now? ⁓ I think I have one, actually. So another one. So my personal take, because of the way that LLMs have developed and everything, and a lot of the different AI areas have developed, I actually think a lot of the value

of AI from a GDP economy, et cetera, perspective will ultimately be socialized. I don’t mean that as in the government will. Yeah, I don’t mean the government will take it and redistribute it. I don’t mean like something will happen from that perspective or socialism will suddenly take over the US or something like that. What I mean is in terms of economic theory and whatnot, you can either have excess profits be captured by specific corporations and companies.

Grace Shao (1:09:03)

What does that mean?

James Wang (1:09:25)

which is frankly as a VC what I’m trying to invest in and basically have essentially monopolistic power, whatever, and base essentially have a lot of rents from society gathered towards the corporation or the company, or you can have a go to labor or you can actually have that value be socialized. Meaning because of competition, because of diffusion of the technology, because it can’t be controlled as much, it just improves society’s lives.

and isn’t actually excess captured by any single company. Even though like we have these huge model companies, they’re absorbing a lot of money, all these different things are happening. My personal take is like, they don’t actually have such strong barriers. Do I think OpenAI will go to zero? No, I think they have a pretty strong consumer brand. Do I think Google will go to zero? No, they have a lot of things to like distribute out. There’s a lot of uses for it. The companies will still survive, but they won’t become like essentially like world like consuming companies in the way that some people have talked about AI or talked about AI as in it’s a sector where a couple of large companies will suddenly take over everything. I actually think AI will diffuse within the economy quite a bit where we’ll use it in our everyday lives, but we won’t necessarily need to pay a company a huge amount to do it. For example, in the future, you might have edge models that just run on a very like a fairly powerful inference chip on your smartphone.

And you don’t need to pay ChatGPT or anyone else for that. It’s just something that makes your life easier, better. And it’s just there. So that’s one of my takes. I actually think the majority of the value hard to measure as that is will probably be socialized.

Grace Shao (1:11:07)

That’s really interesting. think that reminds me of something I wrote about recently and I think we engaged online about this as well, which is ⁓

the diffusion of AI will in some way look like the diffusion of internet, where it’s not like we just think of four companies as internet companies anymore, but even the tangible real world. Like, you you think about food delivery, you would have never imagined a food delivery company is an internet company. However, it is an internet company these days, whether it’s Food Panda or, you know, like Maytwan or, you know, Seamless in the US, that’s actually like...

not a physical world business only, right? And like when you think of a ride hailing, when you think about even like, I don’t know, apartment hunting, whatnot, it’s not limited to just the physical world. Internet companies actually encompasses all these things that we do. It’s just become the infrastructure. So you’re saying AI essentially will just be part of everything we do and it’ll be empowering everything we do. And it won’t just be limited to like the five companies that we think about nowadays. Yeah. Cool.

Thank you so much, James. Really, really, really helpful, really insightful conversation. And I really enjoyed talking to you.

James Wang (1:12:16)

Enjoy talking with you too, this was great, thanks so much, Grace.

Grace Shao (1:12:19)

Thank you.

AI Proem is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

In this episode, I speak with Bei Zhang, VP of Growth at Tanka, about the company’s mission to empower AI-native founders. The conversation covers why persistent, organization-wide memory is the missing ingredient for truly proactive agents, how Tanka stitches together chat, email, calendars, and documents into a single “remembering” teammate, and what agentic work could look like over the next 12 to 18 months. We also take a closer look at the future of founding teams and how agent tools can enable a super-individual way of working without losing control, auditability, or taste.

Tanka sits inside a three-layer stack incubated by Shanda Group. EverMind is the AI infrastructure arm that builds a long-form memory orchestration platform. MiroMind is the research lab, built on Qwen models, focused on long-term memory and reasoning. Tanka is the consumer-facing agentic workspace that applies those capabilities to help startup founders run their day-to-day.

All three were incubated by the family office of Tianqiao Chen, the Chinese internet entrepreneur and investor behind Shanda.

In today’s world, there’s no shortage of information. Knowledge is abundant, perspectives are everywhere. But true insight doesn’t come from access alone—it comes from differentiated understanding. It’s the ability to piece together scattered signals, cut through the noise and clutter, and form a clear, original perspective on a situation, a trend, a business, or a person. That’s what makes understanding powerful.

Every episode, I bring in a guest with a unique point of view on a critical matter, phenomenon, or business trend—someone who can help us see things differently.

For more information on the podcast series, see here.

Topics we covered:

* Tanka’s Mission: to empower future AI-native founders to transform their ideas into successful businesses swiftly and efficiently.

* The Problem Tanka Aims to Solve: Founders often struggle with information overload, with critical insights scattered across various platforms such as Slack, Google Drive, and numerous AI tools.

* How Tanka Works: Tanka’s unique AI memory framework.

* The Team: Tanka’s diverse team is rooted in the heart of Silicon Valley, comprising individuals with rich backgrounds in big tech and startups.

* Competition is not with general agents—focused and niche market.

* Connecting Founders with Investors: It actively seeks to connect founders to investors, creating a community and offering consulting services as well.

* Risks of Using AI agents: Human quality control remains essential; a hybrid model is a sustainable, long-term work model.

AI-generated transcript

Grace Shao (00:00)

Hi, Bei thank you so much for joining us today. I understand you lead Tanka’s growth right now. It’s a very, very exciting startup. I’ve heard a lot about it. Why don’t we start with your role and just tell us about the company, Tanka, the founding mission, what problem you guys are trying to solve, and just a bit about the team.

Bei (00:16)

Sounds good. Sounds good. Hi, Grace. Thank you for having me. Hi, everyone. My name is Bei. ⁓ I lead the product and growth in Tanka. Before I joined Tanka, I had been in various roles in different AI and SaaS companies, mostly in the GTM function. So what Tanka is about? Tanka is on a mission to empower the future AI native founders to go from ideas to founded very fast, very efficiently. And the core technology we’re putting behind the Tanka is the long-term memory behind the agents. End of the day, we’re trying to create a proactive companion, or we say that AI co-founder, because compared to typical AI chatbots, we are putting more power behind our AI agents that can remember all the conversation, remember all the relationship, and eventually can be the proactive.

AI partner to propel the founder to move as fast as possible. So that’s essentially our mission. And we’re hoping we believe the future is the world of super individuals and the lean teams. We’re trying to make the Tanka to be the powerful operating system for the future startups.

Grace Shao (01:25)

So I think there’s some fun and irony in that, right? what does it mean really when you say it’s an AI co-founder? Like for someone like myself, I’m a independent or I would say like a founder of a startup, I have a small team. What is Tanka really helping me do in a very practical sense?

Bei (01:43)

Yeah, yeah, great question. We are essentially the kind of startup, so we help ourselves, right? We’re trying to leverage the resources to help others too. what it would mean, maybe we’ll take a step back to get down to the problems we’re trying to solve. Essentially, we being the center of the Silicon Valley, we’ve been hanging out with a lot of founders, or a lot of individual, a lot of lean teams, a lot of them are just like you, Grace. ⁓ You are a super individual. We also have friends being just a three to five person teams. And the common problem we’re seeing they’re facing is the highly scattered information, overload of information, a bloat of different AI tools, and a very spread of key knowledge across different platforms. So even though we’re saying we’re putting the many of the platforms are wonderful. You got all the nice conversations on the Slack. You got all your documents in Notion and the Google Drive. And there are some offline chats. I’m sure there are valuable informations embedded into various GPT tools or AI chatbots. So the core challenge is not having the right tool. The core challenge is when founders are all of a sudden going from a single threat, trying to take on the world, trying to build a business, the tendency is that there is overloading of the information from all kinds of directions. Because for example, we’ve had a very good friend being a very technical researcher in Stanford. But the moment when he or she step into the founder role, he or she will have to handle not only the product, but also engineer the sales, the marketing, the product dev, legal and tax and BD, right? All kinds of stuff going on. So essentially having all those information scattered in different places create a few effects. Number one, it create a huge overload on the human brain, right? Nobody can process the information so effectively. Especially, we even come across multiple founders doing multitasking because they are trying different ideas, right? Which they will just multiply the pins. And separately, when the brain is overloaded, it instantly distracts the founder from the core duty, which is building the product. So that is causing many problems to be happening. It is causing key information getting lost. It is causing one part of the valuable information not necessarily getting fit into the other nice tools or very powerful AI agents, so the outcome isn’t as optimal. It’s far from it, right? The outcome is far from optimal when they’re trying to make a progress on the project. So that’s the mission. That’s what we’re trying to solve in Tanka. So in Tanka, here are a few things we’re trying to tackle the problem. Number one is the AI memory. Without putting the fancy word out here, just thinking...

As of you have, let’s say, today, whichever, most of the AI tools are not really memorizing your conversations. Because when you open a window, it has a conversation with you. But the moment you close the session, it doesn’t really record anything. So the next conversation is new. So with the 10Cut AI memory framework, all the conversations and all the documents you put in the tool are automatically compressed, stored properly, and also stored with a high fidelity so that when you have a conversation once, the future conversation will always remember what you had before. So it put a piece of mind to founder’s head so that you know there is a trusted partner that never forgets anything. So every company is about moving forward, not to remember what happened in the past. So on top of that, we’re adding the connectors, making sure Tanka can digest information not only happening within Tanka, but also connected from other sources as a deep memory and context. And with the memory, we’re able to put in the right AI agents, whether to produce the business plan, whether to just do the deep thinking and a deep conversation, or whether to produce an investor-ready pitch deck.

They are all based on the actual information in greater details, without you having to chase across all different things. So that’s what we say. That’s the actual specifics we’re putting in behind the tanker, because we’re not calling that just, we want to go beyond the typical AI assistant, because when we say AI assistant, meaning there is some, it’s a reactive, right? There is a AI sitting there and waiting for me to ask the questions or waiting for me to give the proper prompt. So we almost have to treat the typical, even for the very powerful AI chat bot, we have to carefully curate. We have to carefully protect the conversation, making sure it doesn’t generate anything wrong because garbage in, garbage out principle. But with Tanka, because the more you work with Tanka, the more Tanka knows about you, we almost can forget about prompting. It is an actually intelligent person sitting right next to you as a founder. So whenever the conversation happens, we just keep marching forward. And we’re even building more of a proactive AI functions because now that Tanka knows everything, what do we have happening in theory? You should know what I need to do next. even before, in theory, even before I ask,

Tanka to do anything, there should be more proactive actions. For example, hey, I need to follow up with certain investors. I need to update the pitch deck, for instance. Some of them are already realized, and many are definitely on the road as we speak. But that’s what we mean by AI co-founder, because we want to essentially have an AI that can essentially propel you to go forward instead of just waiting there for you to comment the way I do things for you.

Grace Shao (07:43)

That’s super interesting. think to me, when I heard that, I was like, that’s going to be so helpful for me. Cause like you said, there’s so many to do things on the to do list every morning. And then if someone’s actually proactively reminding me or getting things done, that would be really helpful. I first want to talk about the team.

First before we get into the product. Just like I understand you guys have a pretty diverse team. A lot of you guys, ⁓ including your founder, came from even ex big tech. How did your team come together? What’s the background? And I guess what is your edge right now making an agentic tool like this, especially with a lot of even the big AI labs are pushing out agentic tools. Like what is your niche and edge?

Bei (08:05)

Yeah, yeah, great question. So you’re right, we’re a very diverse team. We’re headquartered in Redwood City, California. We do have a global team across different parts of the world. But the core leadership and the product team are right here located in the center of the Silicon Valley because we are a company building for the founders. We want to be where our customers are to shine light on a few other things you covered. When Tanka was born, essentially it was born within a family office that has been actively curating multiple companies and also has been actively investing in hundreds of early stage startups. All the memory problems and all the context switching, all the information overload are very much experienced firsthand, both for the funding members within the family office and also being well observed by the company, right? The family office has been investing and curating in. So it’s a common problem that hasn’t found a solution yet. So that’s where I would say one of the edge is our deep understanding.

We’re not an enterprise tool and we’re not so much to a pure consumer tool. We’re living in a breathing in the startup world because the people has been working in the company or surrounding the company has either been advisors, investors, ex-founders of this kind of startups. So we know the problem from a different angles. So that’s number one. And number two is you’re absolutely right, the CEO, Kisson.

She came from a Meta, from TikTok. So definitely had a good discipline and a very structured approach from well-formed companies. She also co-founded another company that has a similar form of Tanka. So she brought in tremendous discipline in both the AI agent and from 0 to 1 and from 1 to 100 scale.

And I personally come from Grammarly. I happened to have an experienced growing company at a scale and also helped establish the B2B function from the beginning. And other than that, we do have ⁓ members coming from various startups. So we have all been experiencing the problem, first hand, left hand, right? So that gives us a deep understanding on what we want to solve for ourselves.

Grace Shao (10:53)

But $29 a month is quite steep, let’s be honest, especially if founders are cost-conscious. I want to understand what was the thinking behind that. And again, how does it compare with peers, even more general AI tools like Manus coming out of Singapore right now, obviously, as well as the incumbents that have been integrating AI into their apps like Slack, Salesforce, Microsoft Teams, even Zoom AI companion, right? Like in some capacity, they’re all trying to become a more proactive, I guess, whether you can call it a co-founder or a colleague per se, they’re all trying to be there to be more present to help you actually get things done, right? How do you compete with such an array of competitors, essentially?

Bei (11:36)

Yeah, yeah, good question. So to your first question about pricing, we put out a pricing more to create ⁓ a sense of familiarity to begin with. So purely on the number, I think it’s a mid-tier. It’s not that high. It’s not that low either. But it’s something people can, our users can correlate to.

And if you look at our free tier, we actually have a pretty generous free tier. We have daily bonuses. I think for lot of users to get a feeling, the free tier actually can get a lot done to truly feel the memory behind the agents. And also, separately, we’re paying much less attention on the pricing versus our attention on the value.

Because at end of the day, what our users weigh in is how much benefits, how much value they are getting out of the tool. So we’re just putting the pricing as a stake in the ground. We’ve been doubling down on understanding what our users need. They need a collaboration, so we built the AI agents in the chat to empower the team.

They need a generative function to turn the conversation into the actual shareable documents. So we did that. We made a very smooth process to go from the chats and the team conversation into the outcomes without you having to reprompt. The users are also looking for more help in the fundraisin, related features just so when they are ready for investor conversation, they can get it funded faster. So we have a whole pipeline of efforts to empower the founders to realize the benefits. in that, our goal is to make everyone feel like the price is a huge bargain. So that’s something we’ve been actively validating. And also separately, to your point, there are it’s an agentic world, right? Everyone, every company, whether the big ones or whether the startups are making various kind of AI agents. We do keep an eye on a lot of the big names, like you mentioned. I do have a lot of admirations to the great tools. But at this point, our belief is that in this age, the AI tool will come out in different formats and different forms.

So I like to think of them as inspirations and role models, right? More so than the competitions. If they are doing something similar, right? We would say, how can we fill our own gaps, right? How can we do better than them? But often than not, actually have way more gaps. We think even this big names are not even addressing between on the path, right? Between the ideas to startups getting funded. So we’re hyper focusing on filling the gaps more so than worry about the competition. Because we believe the world is big. The world is big. In the future, everyone will be a builder. Everyone will be a founder. If a user don’t use us, it will not be because of a competition. It will be because we’re not delivering our promise and not creating the value for the users. So that’s where our minds are, mainly.

Grace Shao (14:46)

So instead of trying to compete on distribution reach right now, you’re really focused on serving a very niche kind of audience, right? And then really just delivering exactly what they need instead of a general mass audience.

Bei (14:56)

That’s correct. We’re not trying to build a tool for everyone. That’s the job for the big tech. That’s the job for Tech GPT and Cloud. We are in the center of the Silicon Valley. We are hanging out with all the founders who are using all the tools you’re mentioning, but are still struggling in pushing the ideas into tangible business plan. And even for serious entrepreneurs, they are very struggling in getting connecting to the right investors and getting funded very efficiently. So we’re just hyper-focusing on this persona. Because again, we deeply emphasize wisdom because we are them. So if we get this part of the job done, we’ll be very proud of this. We’ll be very proud of our efforts.

Grace Shao (15:40)

Actually, one thing you just mentioned, how do you connect these founders with investors? What’s the strategy there? Because that’s not a product strategy. Is that just your connection, your network?

Bei (15:50)

More so than that. So there are multiple approaches. ⁓ number, think about this in a few different approaches. So number one, this is actually interesting challenge because our founder friends are, most of our founder friends are struggling looking for investors and most of our investor friends are still struggling and looking for quality startups, even though they might be in the same room. So that’s still a ⁓ friction. we tackle this in a few different layers. So many of the founders are not effectively connecting to the investors because they’re not ready. They’re not ready. first, we want to make sure Tanka has the capability for them to chat with the team, for them to carry through all the conversations, and making sure all the minute details are reflected in the business plan and the pitch deck so they appear. They are more buttoned up.

So that’s where we do the effort in preparing them to be investor ready, because investors are ready in the other end of the room. So that’s low-hanging fruit. And then separately, we are very active in the Bay Area funder communities. ⁓ So if anything, we have no lack of is there is an abundant funder communities here in the valley.

And we’ve been actively in the community facilitating the conversation. We’re inviting investors to give advice on how we can build a tool to better empower the founders. We’re doing this in different directions. So in a way, by having a presence in such communities, we’re already acting as a connector between the two parties. And furthermore, what do we do have on the product roadmap. our features like investor database and the investor matching, because that’s a low-hanging fruit. We do want to provide the founders more value by making it very easy for them to see that based on their business plan and the sector, who might be the right person they should be talking to. we are also evaluating the options such as the data room analyzer or the even warm intros because we’re even discussing with the actual human expert fundraising agencies as a potential layer because we do believe this is, AI is not ready to take over the world yet, As awesome as AI can ever be, humans do bring tremendous amount of value. So on a needed basis, there needs to be a human layer on top of the AI workflow.

And even if the human layer just evolved for 10 % of the time, we believe that’s where potentially the 90 % of the value may come from. So this is where end of the day we foresee we likely will build ourself into a hybrid solution where 90 % are conducted by the AI or focusing on this path addressing the problems many of the tools are really not addressing specifically.

And we’re connecting the human brain, the different part of the party much, much closer in solving this problem. So yeah, does that make sense?

Grace Shao (18:58)

And you know where else founders should be talking? They should be talking on my podcast because that’s where investors are listening as well and media is listening. And that’s how you get your story out there as well.

Bei (19:07)

They should. Investors should be listening, too.

Grace Shao (19:14)

Investors are listening. Actually, my main audience are investors in the US and Europe. I think, you know, interesting founders should be DMing me now. But on a more serious note, I think you just talked about like, agents can do what 90 % of work, you still got to have 10 % of human quality control, right? So end of the day, what are things at least at this point, or the next, say, 12 months, we can delegate agents, what are things that we still really need that human touch or humanity to kind of guardrail, the kind of progression of technology or our workflow or the usage of AI.

Bei (19:49)

Yeah, we’ve been thinking this day in and day out. So definitely when it’s related to the information gathering, information collecting, the document generation, document refinement, and web scraping. So without saying the features, that basically meaning how you turn from your conversations and inputs, documents, team chats into the pitch deck, into their data room documents, and how to scrape online, how to go to the linking. Those delegatable missions, those missions that tend to be competitive but yet time consuming. If it’s a delegatable, if you can put into a ⁓ SOP or standard operating procedure, we should try our best to let AI to do this as much as possible.

However, we do acknowledge that sometimes it takes a lot of judgment in this process because when the funders are so early, would the investors invest into the project or are they investing into the persons? Most likely, earlier they are, the earlier the investors are putting their weight on the persons. But many of the persons’ attributes and experience are not quantifiable.

So there are certain things that I cannot build into the AI agents to automate everything. So that’s where we do need a human to better probably connecting with the macro, better putting in the latest reflections, and better just to step in, making sure we’re not misjudging certain startups in either of the directions. And also separately, I would say, we also, Even with all the AI tools out there, we also had very, very top-notch founders who are deeply in the research world. So they just don’t have time. They are very busy. They do want to focus on building their product. Can they learn how to do the whole fundraising business plan or so? They surely can. But it’s more valuable for them to focus on what they do best.

That’s where I think sometimes often it just makes sense for the human layer to just step in and take it over. And it could also be entirely 100 % human touch, which could be well suited for the situation. But just wanting to make it possible whether the human touch is 0 % or 10 % or 100%, it is how this startup works. And we should build our product to be seamlessly connected and adapted to the reality here.

Grace Shao (22:22)

And I think it’s important to kind of note, like, you know, as you mentioned, as we’re all hyping up the AI agents right now, there is some mindfulness to be said to have to about the potential risks, right? So when people are using AI agents, I think this is as an AI agent question as a whole, not just Tanka but who audits the process ensures there are no mistakes, right? When the machines are starting to complete tasks, how do we actually ensure or how do we human ensure that we minimize the mistakes and the risks that they may come with.

Bei (22:55)

Yeah, it’s increasingly a more critical question as the adoption rate for the AI are increasing. So I don’t have a perfect answer. I don’t think anyone has really found the answer yet. I would say it’s the process. Process meaning when we’re building the product, because we’re building Tanka to be very deep thinking, deep researching, and working on very, very serious projects.

We try to use our best model, most expensive one that does the deep thinking and the reasoning to the best extent. So we don’t try to save money using the cheaper model for faster speed, ⁓ which might be introducing more errors. We’re carefully balancing that. We would rather deliver higher quality at a higher cost, but for higher quality. So that’s number one.

And number two is because that’s really actually where the memory comes in. Whenever we build a 10-cut AI to help brainstorm with the founders on next steps, we make sure it all ties back to the prior memory or it ties back to the traceable sources. for all the conversation and the generations, there is a link back to where you can point out to.

But that being said, it’s not 100%. It’s not like we can disregard any human efforts not to look closely. We still are constantly calibrating, and sometimes errors happen. And that’s even because the LLM, sometimes because the core server, it has variations. Maybe a question from the same LLM vendor may generate different answers.

One is more correct than the other one. So I would say it takes both efforts, even though that’s why we do want to emphasize the value of the human, because here’s AI. And we as a human, we still need to be very carefully guarding our own outcome. And then we introduce the human expert to further enhance the quality. I mentioned a lot of fundraising, and we actually have a lot of friends and mentors and advisors from other areas, such as sales and go to market, tax and legal, who are actually ready to engage and looking to find ways to help out the founders. So we’re not building us as a marketplace yet, but essentially we do want to, our vision is we do want to make a Tanka to be the center console where the founders work with Tanka, but also using other tools where it applies. We’re not here to replace anyone.

And we would definitely encourage or we may build a bridge between the Tanka with the human experts so that the human and the AI and human harmonically work together to further minimize the hallucination and the errors.

Grace Shao (25:40)

That’s really interesting. didn’t realize it’s kind of like building up an in-house incubator or like a consultancy, right? Like you have Tanka as your main touch point, and then you expand into your human expertise. Actually on the technicalities, I want to ask what models are you using and how is that decided by the agent? What I put in a prom when I’m using your agent, how does the backend look?

Bei (26:00)

Yeah, so I’ll say, maybe without disclosing a specific model, we do use a combination of the top tier models. maybe that’s the best way to say it. Using the AI memory, I was too aspect. The AI memory layer is built a little differently. It is called EverMind. It’s actually went open source a few days back. So we built our own prior proprietary and memory layer using a set of the algorithm. And that’s one. And when we build our Tanka AI agents, we do have a router option. We do build a AI. We do have a few preset prompt. Whereas depending on the type of the questions and depending on how the different steps of the agents that can execute, you will automatically pick the best model for the task. So it’s not just one, one deal, right? The kind of large language model will vary. It depends on whether you’re asking to generate a rough idea or whether you’re generating a very buttoned up business plan. So it’s different. And separately, I do want to say because of the memory, that’s where things are a little different, right? So because we do have the AI memory,

The large language model is capable of working with ever evolving context and the memory. So even the same question would absolutely yell the different answer the more you engage, the more you evolve with the AI. So I would say the LLM is a commodity. They’re very powerful. They are the necessity. But that’s where at the end of the day, we do think it’s probably going to be safe, whether you’re using Google or OpenAI or Cloud. At some point, it’s going to be indifferentiable, So that’s where, how to make sure it works for you, right? Not for a general purpose. It’s more critical.

Grace Shao (27:49)

That’s interesting. think that’s what a lot of the AI agents companies been saying as well. Like eventually, you know, the user experience will not, the users will not be able to actually differentiate which model they’re using, but it’s really just on how the interface interacts with the user and if it’s for a specific task. So I kind of want to go in on the product itself. Walk us through the product surface. Like, what is the experience like when I’m a user, I’m a founder, when I go on Tanka what should I expect?

Bei (28:16)

Yeah, we put in so much sense into the product, but if we, let’s say, we simplify, as a founder, you go into the Tanka, first of all, there is a place you can work with Tanka agent one by one basis. So on this cases, it’s essentially not too crazy different compared to the other AI agents out there, right? You still interact with the agent, you still ask all the questions, right?

Further develop your initial idea into a very buttoned up plan and further refining and fine tuning on that. Again, the main differentiation is ⁓ our window never closes. Our window stays always on and never worry about missing any information. So that’s the one. And then let’s say you as a founder, you get an idea from ⁓ a raw impression into something more tangible, you need to work with your team, right? And if today, whether the team is your co-founder, or whether it is your friend or your son-in-law, right, advisor, there needs to be a joint effort because often the wisdom come up in the conversations, right? So that’s why we have a second portion of the tanker to be a chat, right? Whereas we, whoever you invite into the tank to discuss the ideas, to hear the feedbacks, whether positive ones or constructive ones, and whether you both share or you all share any external references. All those conversations are precisely memorized and processed to be the high definition by the AI agent. then when

That’s essentially where ideally your business plan will evolve from your own work. And with the other AI agents, you would have to reprocess the information. You will have to bring all this conversation into a prompting and making sure, let’s say, that GBT understands what you have talked about. But it was tank up because the AI is sitting there. The AI is sitting there with you in the conversation. After you finish the conversation, after you are aligned,

You and your partner or your mentor are aligned on certain solution. Well, you can simply tell the tech to say, go make the next version. In that case, there is no transfer of information. And then there is no loss of communication in between. So that’s the next step, because we see the collaboration being a very core part of the founder. Very few people can pull off the one person team, even for one person, assume, right? You as a super individual, you probably collaborated with many, right? To develop your own business, right? And the last but not least is we are building Tankard to be a very open platform, right? Because this is where we fully acknowledge that everyone will probably use some other tools, whether it’s Slack, right? Whether it is Minos, right? My favorite tool. Again, we’re not trying to compete, right? We’re trying to say, if those critical contacts happen in other platforms, we want to make sure there is a way to bring those contacts into the Tanka so Tanka agent can sync with more deeper memory in mind and thus generate more high quality contents, right? And then the other direction is also true because we actually keep the memory well organized. If at some point the organization or the startup outgrow the Tanka capability, and we are building the MCP to make sure all the memories are exportable to the next tools you’re trying to use. So we are here for the specific purpose. And then there is a beginning point and there is an end point. We’re not trying to do everything. Again, we try to do the best in the part of the problem we’re trying to solve.

Grace Shao (31:59)

That’s super interesting. I was just going to ask you, where do you think founders can outgrow Tanka? Because you’ve been really focused on saying, helping them out in the very early stages. it’s interesting that you’re quite mindful that eventually, if a company grows to certain size, there is potential that the company or the founder himself might outgrow your app and they will move on to the next agent, next tool. I guess on that note, I kind of want to end on a big picture question, which is,

Bei (32:05)

So yeah.

Grace Shao (32:24)

What do you think is the future of work for knowledge workers, especially startup founders, what you’re witnessing in Silicon Valley? I think you alluded to this a little bit, that there are more and more of these called super power or super one-person bands, whatever. But what should we expect? Are we still going to see the kind of startups of couple of people with different technical skills kind of coming together, founding a company, to scaling it?

Bei (32:38)

Yeah.

Grace Shao (32:50)

and then becoming a big corporation or are we going to see complete that mode, complete transition revolve.

Bei (32:56)

Yeah, it’s a loaded question. again, we’ve been very actively thinking along the lines of that too. So here are a few things we believe the future will evolve to. Well, there definitely will be big organizations. That’s just the case. Some businesses are better to be at a bigger scale. Let’s say if you build a robot company, you better be. You need a scale. However, we do see that with all the tools empowering people to go from ideas to the apps very quickly, we definitely see there will be exponentially more super individuals. And when we say individuals, it means either one person or either three to five person. Because eventually, everyone, we do see the traditional roles being very blurred, right? There will no longer be like a PM or front end or back end or marketer, right? Essentially one person likely that’s gonna pick up multiple roles, right? I assume, Grace, you probably were many, many roles at the same time as the owner yourself. I think that’s incredible. And then there will definitely, many of the businesses don’t have to be that big. We do see many companies will probably stay it’s pretty small, right, 5 % or 10%. For instance, Gamma achieved a $2 billion valuation at 50%. That’s incredible. And I think there will be more and more companies like that. So that’s what we are inspired to solve for them. And also, adding one more thing is we do think the future collaboration will be multi to multi, right? That meaning is no longer going to be one person being employed by one company for a long time. Because hey, when everyone can do so many things, if that person has a capacity, why couldn’t he work on multiple projects with multiple teams? That’s also where we are creating the tank to be not constrained by an entity. You don’t have to be the same entity because we fully expect anyone can work with anyone. And we want to embrace that and empower that too. And last but not least, again, there are many good thoughts. I think it’s probably a book worthy if we had more time. So I do think this is where, for the first time, in AI can, in the past, in order to value whether the workforce or organization, whether it’s effective, you kind of have to wait until the quarter end or year end to see the outcome, to see that. Because many of the information are not really recorded. But now, because everyone used so many tools, and also AI has a memory, and AI can understand how things work, I think the efficiency will be exploding. Because the AI is able to catch where the inefficiency, where the blocker is happening. That’s also, again, that’s why we built AI to be not just one-on-one, but to be in the team, just so AI can discover, right? It can observe what’s working, what’s not working, and making sure that the team always work. Whether your own team or whether the cross-functional team is always in optimal status before too late to essentially the performance review happening every second. So that’s also back to the super individuals, right? The super individuals can measure their own success in real time and furthermore be more successful.

Grace Shao (36:21)

All right, Bei, we’ve had a wonderful conversation. I have one last question for you, which is a question I ask every single guest that comes on my show. What is one differentiated view you have or something unique you believe in about the industry, about the future of tech and AI, or even something just in general in life?

Bei (36:37)

Let’s see, I have a couple, but I’ll pick one. even as an agent, we’re probably, I think it ties back to our conversation today, right? We are building, I am actively building the AI product, right? So we want to build a co-founder or even a super powered AI solutions. But I do want to acknowledge that the penetration and adoption of the AI in the real world is so, so low. And chasing after a technical advantage, going after, I think ⁓ sometimes it’s just almost a wrong direction for builders to say, let’s make this PowerPoint generation even more smoother or nicer. And while ignoring that, there are massive amount of human workforce are not even closely in leveraging even basic AI to do things. They are struggling with the basic data scraping. They are suffering with the basic information gathering and to be truly embedded in their workflow. This is actually tied back to our chat in the whole fundraising journey. I we’re talking about the most capable, the smartest, the bravest, the most ambitious founder who can build everything. But I mean, why are they still struggling in knowing where to find all the investors? Who is the right investor to work with? Am I ready for the investor conversation? What else do I need to prepare? How good is good enough? And what to anticipate?

Why there are so many basic questions that are not solved. Sometimes I think it’s a, I don’t know whether it’s a differentiator. I just want to, we are doing practicing ourselves. Sometimes we try not to be ⁓ bad at in how we can build this tool to be better than the other competitors. But we go back to the basis on what problem are we solving? How is the problem, how people are tackling the problem today and how we can leverage the technology to best solve the problem. Because I definitely observe when we go chase after the technology advancement, we’re going after like 0.1 % improvements. But when we go back to the basic problem resolution, when we look at how the real world is being operated, we’re looking at like 90 % of the problem are not even remotely empowered. So that’s where I’d like to put out here.

There is still a long way to go. And there are so many things to be built. So I’m very excited about the journey and all the possibility and all the value we can bring to the community.

Grace Shao (39:11)

Thank you, Bei. That’s really thoughtful. And I think that you do highlight a point where I think when we’re all so embedded in the tech and AI scene, we assume people are all adapting and adopting it. But to your point, actually, the general mass is really not up to speed with it. And there’s so much work that needs to be done in terms of educating them and actually working together and actually a lot of issues are not solvable by technology, but it still requires that human expertise. So really appreciate that. Thank you so much for your time today. Is there anything else you would like to share with us before we hop off?

Bei (39:44)

Well, first of all, thank you for the time. I love all the very thoughtful questions. It’s been a pleasure chatting with you and I’m grateful for the opportunity to organize the mind and the share with you and your audience as well. The last thing will be just any recommendation, any suggestions is welcome from you. I I hope this is a...

This is the start of the conversation, more so than the end of the conversation. And again, you’ve been a super individual. I want this product to be helpful for you. And also, I would love this product to be helpful for your audience, whether they are investors or they are founders. So I’m just putting, I’m definitely very, very open. We’re a sponge. We’re a sponge. We’re here to take on any suggestions or feedbacks and that’s the only way we can get better and really focus on the right problem to solve and we need everyone’s help. So thank you, thank you, Grace and thank everyone in advance for all the nice thoughts.

David Fishman is a Principal at The Lantau Group who advises on energy development, infrastructure, and electricity markets across East Asia, with a focus on China. His expertise spans power-sector policy and economics, grid development, project bankability, and transaction support, backed by regulatory and economic intelligence across China’s solar, wind, coal, nuclear, hydro, transmission, and power markets. He has led work on policy forecasting and tariffs, renewable-asset due diligence, China business matchmaking, and green-power procurement for multinationals.

In our conversation, David unpacks how China’s decades-long planning underpins its energy transition and how renewables, storage, and grid build-out are looking to be able to meet AI-era compute demand. We also touch on China’s East Data West Compute and how it leveraged strong geographical planning, as well as discuss the cultural and commercial reasons behind the global retail adoption of solar energy.

For me, the most interesting point he brought up is that electricity used to be bound to scarce resources, but as the saying goes, the sun shines, wind blows, and water flows everywhere. Access to reliable power will become more evenly distributed, which can raise living standards in places left out of prior industrial revolutions - and Chinese technology is driving that change.

In today’s world, there’s no shortage of information. Knowledge is abundant, perspectives are everywhere. But true insight doesn’t come from access alone—it comes from differentiated understanding. It’s the ability to piece together scattered signals, cut through the noise and clutter, and form a clear, original perspective on a situation, a trend, a business, or a person. That’s what makes understanding powerful.

Every episode, I bring in a guest with a unique point of view on a critical matter, phenomenon, or business trend—someone who can help us see things differently.

For more information on the podcast series, see here.

Chapters

Introduction to China’s Energy Landscape

The Evolution of China’s Energy Demand

Nuclear Energy: Pros and Cons

Data Centers and Electricity Consumption

Main Drivers of China’s Growing Electricity Demand

Challenges of Renewable Energy for Data Centers

Geographical Dynamics of Energy Supply in China

Infrastructure Challenges in Southeast Asia

Commercial Reasons for Renewable Energy Adoption

China’s 2030 Renewable Energy Goals and Beyond

The Transition to an Electricity Civilization

Transcript generated by AI

Grace Shao (00:00)

David, welcome to Differentiated Understanding. Thank you so much for joining us today. I have been following a lot of your work on X and LinkedIn and you’re such a prolific writer yourself. And thank you so much for dissecting the industry, but really also breaking down the jargon on energy-related industry policies. So today I think we’re going to cover quite a broad range of topics, but really starting off from the high-level China energy planning, how it came about, and why their leaders are right now.

How that plays into right now, obviously, the energy competition within the AI boom, and then the companies that are backing these developments and growth, whether it’s like PV or solar. But yes.

Thank you so much. So to start off with, why don’t we start with your work and what you do at Lantau Group?

David Fishman (00:44)

Yeah, so I’m, I’m a principal, I’m a principal consultant at the Lantau group. We’re an energy economics consultancy. We’re focused on the commercial and economic aspects of the business of electricity or energy broadly around the region. And in China, I’m focused entirely on the business of electricity. That could mean either working with generators, right, producers of electricity, or those who invest in generation projects.

It could mean working with the markets, the grid, either the physical infrastructure of the grid or the commercial or virtual infrastructure of power markets that help connect power generators to power buyers. And then the ultimate user of the electricity, the end user, which could be a large producer of physical goods or IT infrastructure like data centers. Anyone who has a lot of exposure to electricity as a buyer or seller would be somebody that we work with in our business.

Grace Shao (01:40)

Perfect, so you are the perfect person to go to help us understand China’s energy buildup then. We always hear about China being the biggest emitter, But then at the same time, they’re now the leaders in renewable energy generation. How did that really come about? What’s the kind of dynamic there now?

David Fishman (01:56)

it’s all driven by the need for electricity consumption demand, which has been rising incredibly rapidly at the same pace as the Chinese economy, right, electricity or energy consumption really closely correlated with GDP growth. So as long as your, your economy is doing more things, it starts needing more energy as well. And we went from a period where, you know, the the pace of the growth was even outpacing the ability of energy sector players to meet.

that need. They were not able to build enough electricity infrastructure. They were not able to find enough primary energy sources to meet the growing demand. We’re talking about the 90s, the 2000s. And then in the last 20 years or so, we swung around more towards being in a position of relative abundance on the energy side, where it’s possible to have more energy than the economy is currently calling for. And that’s where we got to where we are now. We built up a huge energy

electricity generation base that was primarily powered by what at the time was the best option, which was coal. Coal-fired power became the backbone of the entire Chinese electricity grid. And because China is huge, whatever is the leading share of something in China becomes just massive in the world. It becomes massive overall. The renewables didn’t really come on the scene until about 2011, 2012 is when the first really strong installation capacity subsidy programs were put into place to really encourage generators to build wind and solar farms and started ramping up the scale of the industry overall. But at that point you already had tons and tons of coal fired generation that was, you know, the backbone of the entire fleet. And that wasn’t going to go away so quickly. So over the last 10 years, we can add tons and tons of wind and solar, but it doesn’t, you know, it only stems the growth of coal. It hasn’t really even started taking away from the total generation of coal fired power. So that’s how you end up having, you know, the largest coal generation sector in the world, the most emissions in the world, and the largest renewable sector in the world all at the same time.

Grace Shao (04:01)

But how did China become so dominant in solar and all the renewable manufacturing? was like scale, was it cheaper capital, cheaper labor, policy support? How do we understand this? Because right now we’re seeing that the US is talking about transitioning to a more, you know, green energy, but like economy.

David Fishman (04:18)

Yeah, well, mean, every to have an industry that is somewhat speculative, relies on new emergent or unproven technology. And in 2011, China didn’t necessarily set out to say, we’re going to go all in on wind and solar, and we’re going to count on this becoming world beating in the next 10 to 15 years. At the time, it was just, you know, we’ve built up a lot of production capacity or a decent amount of production capacity for for solar panels and wind turbines, and we’ve been exporting them.

And now we would like to start installing some of them domestically. But it’s not going to be so competitive or so profitable if we do it right now. So let’s make sure there are very comfortable, good incentives in place so that anybody who builds a wind or a solar farm will be guaranteed a good rate of return on their expenditures. So you start out by saying state support. We need to offer state support to incentivize certain types of things to happen that the market wouldn’t want to build on its own.

⁓ And then we need to be able to, you know, apply pressure throughout the value chain, wherever there would be somebody who’s unhappy about not getting an acceptable rate of return on their activities, generating electricity or producing solar panels or lending money to people who want to do this. Everybody needs to be incentivized to participate in this game. We’re trying to create electricity from wind and solar and it’s not very economically competitive right now. So how do we help them, right? That’s where you get Yes, your subsidies, your state support, your low interest loans, your affordable land access, things like that, all of those things, those help thing scale up, right? Those give you scale. And once you start getting that scale, you start to enjoy the economies of scale, you start to enjoy the effects of competitors on the production side entering into price wars to try to maintain market share. You get these jumps forward in innovation. I’m going to squeeze an extra 1 % out of my solar panels. going to I’m to beat the other guys, right? The scale turns into a bit of a snowball where all these other effective, enjoyable benefits, your economies of scale, you’re increasingly lower costs, you’re increasingly more attractive technology are all piling up. And all along the way, you’ve still got the state presidents hanging out in the background saying, will help, will make sure that your rate of return is acceptable. If things look like they’re getting out of hand, we’ll tweak things so that you still make enough money to keep yourself solvent. And then finally, one really important factor in all of this is the state owned enterprises themselves. The state owned enterprises are mostly involved in the capital intensive section of the industry. So that means building and operating the solar farms and the wind farms. That’s the really risky part of the entire value chain.

And so they’re willing to take on a lot more risk than a private developer might be able to. They’re willing to accept lower profit margins than private capital might be willing to. And that acts as a great big lubricant for the whole system so that you can continue keeping capacity numbers high even if rates of return sometimes are a little questionable.

Grace Shao (07:20)

So, how should we understand this, actually? Most of the energy players, whether throughout the supply chain, are SOEs or do we have major private players as well?

David Fishman (07:30)

Well, so the equipment makers are almost all private companies, right? Your solar panel manufacturers, your wind turbines, your batteries, those are all private companies. They get support from the state. They have subsidies, they have land grants and things like that, but they’re all private companies. And then on the generation investor project development side, you have mostly SOEs. Private companies are certainly happy to participate when they find good opportunities with acceptable rates of return. But for everyone else where maybe the rate of return is only a 6 % return on capital, right? That’s not attractive to private equity. That’s not attractive to a fund usually, but state-owned enterprises are happy to do that kind of work because that’s their mandate. So it really is a partnership between the private sector doing certain things and the state sector doing other things.

Grace Shao (08:21)

That’s quite commonly actually seen in China, Like across a lot of industries. How do we understand the grid mix right now in China?

David Fishman (08:28)

Yeah, so right for for 2024, which is the last year we have full full data sets for I think was something like 58 % coal fired power and that’s been declining as a percentage even as it increases in total volume, right? Because other sources have been growing faster than coal has been growing but 58 % coal and then we had wind water solar

Wind, solar together came out to, I think it was about 32%, something like that, with wind and solar being in the 18 % range and hydro in the 14 % range. Hydro has a lot of annual variability, depending on whether it’s been a good year for rainfall or not. And then the balance, that last 10 % is made up of nuclear, which about five, five, six percent. You’ve got gas to power, which may be two or three percent, and then other, includes things like biomass, waste to power, experimental technologies like that, but still very fossil fuels dominant.

Grace Shao (09:28)

How does that compare to other major economies? We might look at Germany or even the US.

David Fishman (09:33)

Yeah, it varies a lot depending on their natural resource endowment. So the United States has been shutting down a lot of its coal capacity and instead leaning heavily into gas to power. And gas to power is now one of I think it should be the largest generation source of the United States. There’s a lot of gas in the United States. Then you take a large country like ⁓ France, right? France has nuclear, built nuclear decades ago. Nuclear is still the largest contributor to France’s electricity mix. You switch over to somewhere like Brazil. Brazil’s got lots and lots of hydropower and hydropower is the main driver of Brazil’s economy, of their electricity economy. you know, natural resource endowment really matters. Major countries that are still using coal a lot, like China, you’ve got places like China, India, ⁓ Indonesia, those are the ones that come to mind, especially in East Asia.

And then throughout Europe, course, Poland is well known as being a huge, huge coal consumer. But if you’ve got access to gas, you use gas to power instead. South Korea uses a lot of gas. Currently, Japan uses a lot of gas. Gas is very common throughout Southeast Asia, usually imported gas or LNG. So it’ll be quite, quite expensive if you’re using that for electricity, which can sometimes contribute to high power costs, lot of gas in Europe as well.

Grace Shao (10:51)

That was really good context. I actually want to double click on the nuclear topic. It seems like there’s quite a bit of controversy with a lot of the SMRs being like the small ⁓ nuclear plants that are being reactivated right now. How do we understand, I guess, the pros and cons of nuclear? Is it really still quite dangerous for local communities or is it like potentially a big possible solution for us as we’re seeing electricity shortage?

David Fishman (11:16)

That’s like three different, very different questions. So I’ll try to answer them in order there. So SMRs are next generation kind of experimental ideas for nuclear that they could be maybe more cost effective or more flexible in a smaller format, something like 100 or 200 megawatts instead of a thousand megawatts. Right now there are only a couple of SMRs operating around the world for commercial civil power use.

⁓ China’s got one, for example. and mostly in the United States, the conversation has revolved around restarting some previously retired or mothballed but not shut down or decommissioned, nuclear power plants. That now that there’s this energy crunch, an electricity crunch that some of that retired generation that maybe wasn’t competitive in the market landscape of whatever year it was, decommissioned in or retired in, mothballed in,

Currently the market climate has changed that there’s such demand for electricity now in such a scarcity that the buyer of the electricity is willing to finance the refurbishment of the plant and willing to finance the long-term operations of the plant by becoming a buyer of electricity. So when you look at your major tech companies that have just signed an agreement to restart a nuclear power plant, that’s because the operator, the owner of the nuclear power plant has secured a very lucrative contract to sell electricity from that power plant to that data center for a long period of time. On the safety aspect, yeah, look, we’ve had a couple of notable kind of headline grabbing, world attention grabbing accidents over several decades of the nuclear industry’s operations. I always consider it to be kind of like one of those airplanes versus cars thing, right? A lot of people are afraid of flying, although it’s incredibly safe relative to driving on a statistical basis, right? But you know, an airplane crash grabs the headlines in a way that a car crash never will. And that’s, you know, a similar situation with the safety of different energy types, right? A nuclear power incident once every several decades grabs headlines, but the long term damage to human health and livelihood caused by combusting fossil fuels has been immensely larger, incredibly, incredibly larger. So everything has its own kind of trade-offs and how you evaluate it is up to you, but I do invite everyone to think of it as an airplane crash versus a car accident risk scenario.

Grace Shao (13:40)

That’s a very interesting way of framing it. I think like you said, a lot of times the headlines really focus on what’s big or what’s more exotic right? And that’s kind of in the case that nuclear is just not as commonly heard about, therefore everyone actually pays attention to it more. I really want to double click on what you just talked about on the big headlines of big tech buying up, or reviving plants to power data centers. Because when we were talking before this interview, you joked you said,

Hey, look, I’ve been an energy guy forever. No one really wanted to talk to me that much before. For now, all of a sudden, everyone wants to talk about energy, right? We are in the midst of an AI boom. And the bottleneck, especially for the US, or the choke point right now, for a lot of these big tech companies, is securing enough energy to power data centers that are required to power their training or influence whatnot, right? So help me understand this. The AI boom is capital intensive.

So is energy. What, how do we understand the relationship actually between energy and data centers and AI right now? Why don’t we start with that big question?

David Fishman (14:42)

Yeah, well, mean, there are a few other productive activities in the world that rely so much. On electricity as a primary input as the operation of chips in a data center. I mean, maybe the only thing that’s similar is Bitcoin, right? Mining Bitcoin because the operations you’re doing are just constant calculations that require consumption of electricity to be performed. data center operations are similar. In the physical world, the only thing I think comparable is something like ⁓ non-ferrous metals smelting, like aluminum or copper smelting is also just, we call it solidified electricity.

That’s what aluminum is. So data centers are constantly performing actions and tasks that use electricity. It’s a direct relationship to do what it needs to do to be productively useful at all. needs to consume electricity. so from an economics perspective, you just say this is a demand driver of electricity, almost a direct demand driver of electricity. It’s not an aluminum smelter, it’s a data center, but we need more electricity to serve its needs so that it may serve its function, its customers, which are asking for computing power. And so from the electricity sector perspective, I just see that as a number that used to be 1 % year on year demand growth, and now it becomes 3 % year on year demand growth, or 5 % year on year demand growth.

And that’s something where, if we build one or two generation assets, we will meet the anticipated demand for the future. Now it’s, need to build eight generation assets or 10 generation assets, or I can’t allow that large electricity user to actually connect to the grid and start demanding power. Cause I don’t have enough. I don’t have enough electricity to serve them as a grid operator. Maybe that’s my perspective, right? You’re not allowed to connect to my grid, you need too much electricity. And when you’re in that circumstance as a data center operator, you’re saying, well, I got to bring my own power, right? Bring my own electricity. And so that’s kind of the way they’re thinking about it now. If I want to set up my compute in an area that, you know, for whatever reason, it’s beneficial for me to be sited here, but this local grid doesn’t have enough electricity for me, I got to come up with my own solution. I could build my own assets. I could have a captive power plant, perhaps I could support ⁓ local generators to build something and sign a long-term contract with them so that they’ll build the asset, I'll build all the power. Or maybe I’m looking at something like, you know, something more creative where I say, Hey, there’s already an asset. It’s in the grid system. It’s nearby. It’s this nuclear power plant that’s been mothballed for 15 years. Let’s get that going again. I’ll buy all the electricity. Whatever it is, it’s it’s, you know, because of this very strong direct relationship.

A data center operator is a wonderful customer for an owner or operator of a power plant, right? I’m in the business of making electricity, you’re in the business of using electricity, like, surely we can work something out. So that’s where you how you end up with this kind of like, we call it a PPA, a power purchase agreement. That’s the direct relationship that’s established between the generator and the consumer.

Grace Shao (17:48)

Who are you seeing as biggest consumers of power right now amid this AI boom that you’re seeing in China specifically.

David Fishman (17:54)

Well, yeah, it’s going to be your big Chinese tech companies, right? Your Tencent, your Alibaba, and ByteDance, companies like that that just have massive need for tech, you know, and then your emerging AI companies. And of course, the big tech companies have their own AI plays always. And then they’re going to be independent or third party AI companies that are looking to train or whatever it is. And they’ll

They’ll need a lot of that too. So in the IT space, that’s who you’d expect. The big tech companies and your frontier model creators.

Grace Shao (18:27)

And what do you think, if you had to put a number on it, how big is this AI driven load as a percentage in terms of the incremental electricity demand we’ve seen, I guess, in the last three, four years and comparing it to next three to five years of production?

David Fishman (18:40)

So it’s interesting, it grabs all the headlines, but remember, Chinese power consumption is already growing at a stunning rate for every other reason already. So this is just one additional thing. So in the last five years, think it’s been ⁓ a modest amount of the growth can be attributed to ⁓ data center needs.

⁓ Looking at something less than 15 percent I’d say maybe 10 10 15 percent of the growth can be directly attributed to data center needs And then over the next five years I’ve seen forecasts from like state-grade energy research Institute where they’re saying you know of all the different sources that are driving power consumption growth Maybe maybe 20 % will be attributed to to AI so or to data center so not not like nothing, but also not as much as you guess or expect, certainly not compared to other countries where it really is maybe 50 % of the load growth or more can be attributed in that way.

Grace Shao (19:36)

I see. What are actually the top drivers for China’s demand, growing electricity demand, other factors?

David Fishman (19:42)

Yeah, so among those, the same report that I saw that said, you 20 % can be AI. In that same report, it was another 10 to 15 % or so. it was 10 % is charging for EVs as a driver of growth. And then another 10 % was electrolysis of hydrogen. So you can produce hydrogen by running a current through water, essentially electrolysis of hydrogen, which replaces our other ways of producing.

Grace Shao (19:57)

Okay.

David Fishman (20:09)

hydrogen, which are usually quite dirty and fossil fuels intensive. And hydrogen is very useful as a as a input for for production of ammonia or methanol, many other very useful chemicals. So that’s about 40 % of the growth was attributed to those three things, these three emergent sectors. And so the other 60 % of the growth will be traditional sectors. So that’s your heavy industry, and uses tons of electricity all throughout it. That’s your services or your tertiary industry. So you’re looking at growth of AC use in shopping malls and hotels and things like that. And then the smallest portion of that is residential power use. All three of those traditional sectors are all rising in China as well. And in addition to these new three sectors that are emerging as part of the clean tech revolution.

Grace Shao (20:58)

That’s really interesting. I wish we hadn’t time to go into the EV talk today, but see if we do later, but let’s focus on AI first. I actually want to understand why can’t we go full renewable with these data centers right now? What’s the issue with, you know, using renewable solutions instead of traditional solutions? Cause there’s been a huge debate around that, right?

David Fishman (21:16)

Yeah, well, mean, and so when you have a power load, doesn’t matter what it is. If you have a power load that draws on power pretty continuously, the easiest way to meet that kind of load will be something that generates power pretty continuously. It’s not impossible to meet it otherwise. It’s just the easiest, the most straightforward way to meet that load. If it has certain types of flexibility in the way it operates, then you can also start to address it with more flexible generation sources. But overall, a ⁓ data center load is a more stable large load. And large stable loads are really good matches for our conventional generation sources. Our hydropower short, but you know, coal, gas, nuclear, great matches for the way a data center needs electricity. Now we can meet those needs with a combination of solar and wind and storage and flexible generation sources like that. But operating those in that way generally incurs a larger cost, either a direct cost in terms of operating cost of the assets or a systemic cost.

And in order to have all those variable assets in there, we also needed to keep some gas generators on standby. We needed to pay some capacity payments to a battery station, something like that. Those are systemic costs that also usually end up being assessed to someone, ideally the one who caused the need. But sometimes it’s assessed to an end user who didn’t cause the need for that variable generation, but they still paid for it. So when we look at can we meet the ⁓data center needs with renewable, certainly we can. It’s just more complex and it takes more planning and maybe it incurs more system costs and sometimes the system costs aren’t really well tracked. So China does intend to start meeting a lot of its data center growth with renewable sources. We talk about where the new data centers are going in, the parts of the country that they’re going in and there’s this program called the National Hub Nodes.

Which is part of the East Demand West Compute program. And so under this scenario, any new data centers added into those national hub zones need to be consuming at least, I think it’s 80 % of their electricity needs to be renewable. It’s a pretty high percentage. So if it weren’t possible, there wouldn’t be such a requirement, but it is trickier, it’s more complex. And so that’s how China is proposing to drive most of its data center build out now is with these renewable energy sources. It’s a way to avoid this growth of a new load sector resulting in increased fossil fuels consumption.

Grace Shao (23:57)

I see. I’ve heard about like, you know, the challenges with battery solutions right now for the intermittent kind of nature of renewables. Do we have actually strong enough battery solutions right now to solve that issue?

David Fishman (24:10)

I mean, batteries, the question is, can you you scale it cost effectively? Right? Can you can you get your battery storage to be able to cover what two hours, four hours, eight hours? How long of a backup solution do you need there? How what type of gaps are you expecting to need to meet in the context of all the other power that you’ve contracted for or that you have available to you? If if you’re in a region where there’s tons and tons of solar power, it’s sunny all the time. The sun is up most days and it’s producing well most days. Okay, well, you’ve got a solution for part of the day. And then it’s like windy in the evening. So you’ve got a solution for the evening. But how do we cover that evening peak period when it’s not particularly sunny or windy? Okay, batteries, sure. How long are the batteries good for? Can we be sure that the batteries will be charged and ready with no loss of load incident every day? Or at least at a very, very slow failure rate, something like that. When you start doing some type of probabilistic assessment, how many batteries are enough so that we can ensure a failure happens once every 10,000 operating years or something like that, whatever your threshold is, you start to realize, woo, it’s a lot. I need a lot of backup. I need a lot of storage. And so maybe that’s where you run into some of the problems where the project economics might not work. They might not make sense anymore. Ideally, I’m describing an extreme scenario, but ideally you can find a solution that involves not using massive, massive amounts of batteries that you barely use most of the time.

Grace Shao (25:38)

I see. It just sounds like there’s a lot more operational risk, right? I want to understand East data, West compute. From your perspective, like how should we understand that, I guess, from the energy framework? And can you help us understand the geographical importance of having the energy suppliers on Western regions of China?

David Fishman (25:57)

Yeah, it’s absolutely driven by geography. maybe you’ve seen the famous line before, you can draw a line through the center of China and everything on the west side of the line is 7 % of the population and everything on the east side of the line is 93 % of the population. It’s roughly half and half of the country. That’s just where all the Chinese people, all the load, all the industry is, is along the coasts in the east and the south of the country.

And so your need for electricity, your need for, your demand for any energy use of any kind is mostly going to be out there. But the good energy resources in China are in the West. They’re in the North, the Northeast, and the Northwest, and the West. So you’ve got to find a way to get them to each other. You want, you know, the wind blows all day, the sun shines all day, and the mountains are just heaped with coal in Western China.

So we got to connect them to Eastern China. In the case of data centers, you’ve got two options, right? Either we’re going to bring power lines from the West to the East, or we’re going to bring fiber optics from the East to the West, right? So either I bring the electricity to where the load is, or I send the compute to where the electricity is. And they are doing a mixture of both. So this is where you end up with a bifurcation of the types of computing needs that you have.

The short term, very rapid response compute needs to stay in the east, close to where the demand is. And so for those, we bring the electricity to the compute. And then for like a longer lead time, you know, maybe we’re training a model or something like this, we can send it out to the west, send it closer to where the cheap electricity is. So this is a consideration that makes sense for China, because China is huge and it’s got very different ⁓ dispersion of its resources. A smaller country could do it on a smaller scale, of course. I know in the UK they joke about London data computed in Scotland or something like that because they’ve got the offshore wind up there in Scotland. So it’s a similar concept, just in China’s case it has to happen on a continental scale.

Grace Shao (27:56)

I’m also interested in actually how the relationship between the provinces are. How do they work together? Do they have different mandates for each of the provinces or is it just a very top-down kind of mandate from the federal level?

David Fishman (28:10)

Yeah, so in China, is a tiered, a hierarchical relationship between kind of national government, provincial governments, and municipal governments. Generally, you’d expect planning at the more macro level to come out of Beijing for the whole country, and execution is going to be left to provinces and municipalities. So when they say you need to enable cross-regional power transmission, okay.

Like that’s that’s something that comes out of Beijing, but now it’s up for the individual provinces to work out how to do that. Eventually, they’ll execute their trades through the state grid power exchange, which is up in Beijing. But all the negotiations and all the interactions have to happen at the provincial level or even lower at municipal levels. That’s that’s broadly true for for almost all Chinese policy, not just the power sector and not just data centers, but in general, broad strokes laid out at the top and then executional happening at provincial or low.

Grace Shao (29:05)

That’s interesting to hear. I kind of want to shift gear and kind of double click on the private sector right now. You you just talked about along the supply chain, lot of the coin makers are actually private companies. We’ve got the PV makers, the battery makers. think, you know, people in the West will probably know of CATL, Longi, Trina. There were some also even international ambitions of these companies kind of set up manufacturing hubs in the US or even across Southeast Asia. Can you kind of help us understand their global ambitions and where they’re at now.

David Fishman (29:34)

Yeah, mean, so the initially the Chinese market was very initially, they were built in China to be sold internationally. And then the Chinese market got so much larger than everywhere else that many of these equipment makers had plenty to do just selling to the Chinese market. But because of the very, very stiff domestic competition, there’s always that interest of like, well, could we could we diversify a little bit out of this incredibly competitive environment by selling?

broad as well. And so initially they could sell abroad from China again, as some of those trade barriers started going up that really incentivized the expansion into Southeast Asia, into Vietnam, for example, that you could have Trina solar panels produced in Vietnam and exported to markets that were maybe closed, or tariffed for for Chinese exports. And then we saw, I mean, with the United States and its recent tariff policies, that tariffs were placed on the Southeast Asian countries as well. And then specifically if there was evidence of trans shipment of Chinese panels heading to Vietnam and then going to the United States that they would be taxed or tariffed even more. So a lot of that has, you know, eventually ended up coming down to just one place, right? If you’re a Chinese solar producer, and you want to sell to the United States, you’d better be ready to produce in the United States. That seems like the way I’ve talked to a couple of them, and that’s the way they’re thinking about it. If they had production already, they say, okay, we’re gonna keep producing for the American market from our production facilities there. If they’re thinking about starting a new, opening a new factory, well, it’s been a scary environment for that kind of thinking recently, right? Of course, there was the recent⁓ raid on a South Korean under construction battery facility in the United States, right? There’s a lot of concern about, you know, that kind of capex heavy investment for a market that just maybe isn’t geopolitically that friendly, and it would come back to bite you in the end. There was another case, I think, where a Chinese solar panel producer built a facility in the United States, and then it wasn’t going to be eligible for the IRA tax credits and they were forced to sell it to an American company, which is now producing solar panels from that facility. So if you’re a Chinese solar panel manufacturer or battery manufacturer and you’re looking at this kind of geopolitical climate, certainly you’re willing to consider building an overseas factory to get around tariffs, whether it’s in Southeast Asia or even in your target market. But some markets seem like they’re a little bit more geopolitically favorable than others right now.

Grace Shao (32:08)

It’s obviously quite challenging for a lot of these companies trying to sell to the US right now. Just like you said, given the kind of backdrop. Okay, I want to ask about Southeast Asia. We know that a lot of these companies are building out, you know, manufacturing hubs, even data centers in Southeast Asia. A lot of the big tech companies, Chinese big tech companies are now said to be the biggest hyperscalers across Southeast Asia as well. Like they’re buying up all the data centers.

But there are some concerns around infrastructure challenges in Southeast Asia, right? So there have been complaints about, you know, the utility prices rising. There’s issues in Johor, Malaysia, where frankly, local infrastructure such as water, land, roads, it’s not really there yet. How can you, how do you help us make sense of all of this? Like, who are the buyers? Who are the investors? Who are the users? Is this really helpful right now? Are they actually helping the local economy? And are they actually, are these companies getting what they need in those regions?

David Fishman (33:00)

Yeah, so anywhere in the world, including China or even very, very transparent power markets ⁓ like parts of the US or in Europe, it has proven to be remarkably difficult.

to attribute changes in the price of electricity to any one thing. Data centers have become a ready and available target. And I think it’s surely true in some cases that it is the thing that’s driving higher power prices for that region or for that node. But in other cases, I think it would be more difficult to make that assessment. And it’s something that is speculated in the media and then kind of becomes true by default without really having a good investigation into it. I haven’t looked at Malaysia or Vietnam enough to comment whether I could attribute the data center, no matter who owns it, the data center demand to rising power costs. It does make sense usually in an economic sense, right? Your demand load has increased, your supply hasn’t changed, and so we end up at a higher point on this cost curve for more periods of, you know, throughout the day.

So that’s, mean, it’s something that is theoretically a problem. If it gets to the point where the data center load starts to really affect the competitiveness of electricity for other parts of the economy, either the industrial, other industrial segments are saying our energy inputs are too expensive now, or you’ve got residential power costs skyrocketing, you’ve got a developing economy, and you’ve got, you know, residential people can’t necessarily afford the costs of higher energy costs, then you’d expect the national policymakers, regulators to step in and say, if you’re going to be allowed to operate in this way, we need you to bring your own electricity, for example, ⁓ or you need to be willing to cover the increased costs of other sectors of the economy through some type of ⁓ payback scheme or kickback scheme, right? That kind of thing is a reasonable thing to ask of a data center operator when they’re coming in and creating those types of problems for a generation overall. But I mean, remember, they are also creating their own economic benefit. They’re not just leeches on the system, which I think many would argue something like cryptocurrency mining is that you’re just sucking up tons and tons of electricity, and you’re only creating value for a smaller group of people who believe in the value of the cryptocurrency. Data centers, on the other hand, are performing tasks that are considered to be productive and valuable tasks for their clients, for their customers.

Maybe you could argue you think that using AI video generation is not a productive and useful task, but hey, like somebody thinks it’s valuable and useful. Enough people think it’s valuable and useful. So that’s what you weigh the two against each other, right? You’ve created a burden on the grid system, a burden that is having knock-on effects on other parts of the economy. You need to account for yourself. You need to take responsibility for the burden that you’ve created on the system.

But also, you you’re not a leech. You’re not a parasite on the system. You are in fact doing a useful thing and it’s the responsibility of energy planners and economic planners to try to make energy available to you. So it’s a delicate balance. And it’s, I wouldn’t, it’s not fair, I think, to attribute blame to anyone unless they’re intentionally trying to get around policy or screw over other people.

Grace Shao (36:28)

Yeah, I have a question that’s a bit more anecdotal, but when I drive through Europe or China, right, you see like solar panels on private citizens houses across the countries, right? Like, you know, in Spain, Italy and Germany, you really see across China, like Hebei in Songsu, you really see people embracing it as a society. Can I do you think there’s a cultural difference in terms of kind of embracing renewable in the US versus China or Europe or what kind of incentivizes them.

David Fishman (37:02)

There’s a cultural difference, but in the case you cited there, there’s a very strong commercial difference as well. In Europe, residential solar is primarily motivated by the decision making of the people that own the house, that own the rooftop where the residential solar goes in. In China, rooftop solar is driven by developers who don’t see the they see the rooftops as real estate.

So developers go in, they knock on doors and say, I see you have some nice unoccupied rooftop space up there. I will pay you to lease your rooftop. I would like to put solar panels on it and sell electricity back to the grid. So the Chinese business model is very unique in that case. I would say the United States and Europe are more similar in the ways that the mechanism for motivating, motivating residential installations. And then China is off doing its own thing because it has this incredibly effective, but unique scenario where developers are the ones promoting solar panel development to residential users who weren’t very aware of, you know, solar energy at all. And then also, they don’t even have much electricity consumption, frankly. So they’re not super motivated to go install solar panels. ⁓ residential electricity costs in China are very cheap. So it’s not like they’re trying to offset a high power bill.

The whole motivation for the residential sector in China is ⁓ collecting a rent on their rooftop space so that some developer can generate electricity and then sell it to the grid.

Grace Shao (38:31)

I see. I totally thought it had something to do with just kind of this overall societal embrace of new technology and this 2030 renewable energy goal. Can you actually, I guess the last question I have for you, can you help us understand what is this grand 2030 goal of China to really convert, you know, transition majority of the energy consumption to renewable? How do the EV sector, the battery sector, and various sectors related to energy really play a role in this?

David Fishman (38:58)

So the 2030 goal is the peaking goal, the emissions goal. So they want to peak carbon emissions by 2030. And then they say carbon neutrality neutralize the emissions by 2060. So we got to reach a peak, then we got to draw down and neutralize. So by 2030, I mean, maybe we’ll be down to a 50 to 55 % share of coal in the power sector. I don’t know if it will slip below 50%. By that point, it’ll be getting close to it.

And then you’ve got all your other non power sources of emissions, right? Transportation, building, heating, and industry. That’s the major one, industrial inputs. So by 2030, the goal was to just peak the emissions across all emissions generating sectors. Now it looks cautiously like we might have reached an early peak this year. I say cautiously and I hedge my phrasing here because, well, there’s lots of different segments that create emissions.

Power is maybe gonna stay flat, maybe gonna be, you know, peaked by 2025, 2026, but we’ll see what happens to new capacity for wind and solar next year. If wind and solar and hydro and nuclear can’t keep up with the pace of consumption growth, then the only way to meet the additional consumption growth is by using more coal, right? So that’s the power sector. Fingers crossed that they managed to figure it out. Transportation fuels maybe is going to peak in this year or next year. We’re talking about the EVs coming in such massive numbers that they’re replacing petroleum ICE vehicles.

Grace Shao (40:28)

What’s the percentage actually? What’s the percentage of EV cars on the roads right now, do think?

David Fishman (40:33)

It’s you know, it varies widely by province in Shanghai. It’s over 50 % now for sure. Major cities is 50%. But you know, I was recently in, like Shanxi, a northern, you know, Chinese province, and it was probably less than 10%. ⁓ So it’s I don’t know what the number is nationally. It’s it varies so incredibly by city, I think it’s or by province, I think it’s best to look at that on a provincial level. But the idea being that new

Grace Shao (40:38)

Wow.

David Fishman (40:57)

New EVs should have already started outpacing the sales of ICs and that should continue so that we would be able to, know, more vehicles are going on the road but the EVs are being added more rapidly and that petroleum consumption for passenger transportation should be peaking, you know, within the next two years or so. And you’ve still got aviation and maritime fuels and long-distance trucking and all the other things. Excuse me.

And then finally, you’ve got the industrial sector and there’s different aspects. Some are growing, some are rising, know, steel is maybe flat, cement is dropping for the next 15 or 20 years, but hey, petrochemicals and coal chemicals are rising for the next 15 or 20 years. So you’ve got all these different, you know, sources of emissions to consider. That’s what’s all going into that 2030 goal. So if they manage to peak by 2030, like wonderful, lovely, enough of those sources have peaked and started drawing down that we can make up for the ones that have not peaked and that are not drawing down. And then we’ve got another 25, 30 years beyond that to figure out how to get to neutrality after peaking in 2030. If they happen to incidentally peak this year or next year, I don’t think they’re gonna claim it. I don’t think they’re gonna crow about it. I think gonna stick to the 2030 emissions peak, give a little bit of time just in case a little bit of buffer.

Grace Shao (42:08)

Really?

David Fishman (42:13)

But it seems very likely that we’re flirting with the peak a little bit early versus their own targets.

Grace Shao (42:20)

What’s the practical impact on society and the economy then?

David Fishman (42:24)

Well, the ideal goal is to have as little impact as possible, right? That business as usual growth as usual continues, economic abundance of energy is compatible with energy transition. That should be the goal that the only outcome in fact is that the business of the energy transition is good business, that it contributes in fact to GDP.

⁓ installing solar farms and building wind turbines and upgrading industrial equipment is all like good economically productive activity. And so that should be the only impact. That it’s a good impact and that otherwise everybody just goes on driving cars, they just happen to be EVs now. They go on using electricity, it just happens to be increasingly clean electricity. They go on using steel, it just happens to be green steel. That’s the best case scenario.

Grace Shao (43:16)

That’s a good world to be looking forward to. Okay, David, thank you so much for your time today. What is one differentiative you you may have? It could be about your industry or it could be about something else broader in life.

David Fishman (43:28)

One differentiating view that I have.

Yeah, well, I mean, I’ll talk about my other great passion, which is kind of economic development in general. I got into electricity because I cared about development. And I stay in electricity because I continue to see it as important for development. And I am pretty agnostic when it comes to the different isms of the world, the different political science factions that say it’s this ism or that ism.

jWhat works is the best ism, whatever it is, I don’t care what label it has. And my observation right now about where we are as a civilization is that we are right on the precipice of moving from the fossil fuels phase of humanity to the electricity phase of humanity that electricity will be an interim period, because eventually we’ll probably figure out fusion power and harnessing plasma, and that will take us to the stars. But in the interim phase, we’ll be in the electricity age. We went from biomass to mechanical energy to fossil fuels to electricity generated from the natural elements. And that this electricity phase should be the phase where energy stops being additive.

And starts being substitutive instead. All the previous energy phases that we went to, we had to use more energy to upgrade. But to upgrade to electricity should allow us to start using less primary energy. That using wind and solar and hydro and nuclear are less intensive for primary fuels, which means we’ve freed up space for developing nations in the world to start to enjoy a fraction of the energy abundance, the civilizational abundance that so much of the rest of the world is already enjoying. They’re already there, right? As long as we stayed locked in the fossil fuels era, they’re not allowed to enjoy that abundance. They’re locked out of it because we have no more carbon budget to spend. We need to work on this, you know, civilizational existential challenge of transitioning our energy. And for the moment, there’s no more carbon budget for the developing countries of the world.

Getting into the electricity era, the electricity civilization era, frees up that carbon budget, enables them to start pursuing the same type of abundance that everyone else is already enjoying in developed nations.

That’s the great thing that motivates me throughout all of this, that the electricity civilization era is one where regardless of what ISM introduced it or ushered it into the world, is one that is going to be incredibly beneficial ⁓ for humanity. And at this moment, I think I’m working in the country that is most likely to usher in that era. And here’s the controversial portion, right? I think China is the one to bring the world right now into the electricity civilization era. And that will be such an incredible boon for humanity in general.

Grace Shao (46:27)

That’s a really, really interesting way of framing it. I really appreciate that. Thank you so much for your thoughtful answers and just your time and your insights today. I really appreciate your time and just your generosity in sharing with us.

David Fishman (46:38)

Thank you for having me. It was a great pleasure.

AI Proem is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

“So our philosophy here is to integrate the globally optimal models and give users the best results.” — Hang Yu, Head of Product at Qoder, Alibaba

This is the first episode in a series of founder and builder dispatches, featuring interviews with the people creating the future. If you are a founder, builder, or investor in this space and would like to share your story, please reach out.

Today, I am joined by two guests from the Qoder team at Alibaba: Hang Yu, Head of Product, and Christian Hu, Head of Global Marketing and Operations. The Qoder team launched just over two months ago, joining the likes of Cursor, Warp, and Copilot to make coding more agentic, so today we get to learn from them directly about their unique positioning being part of the Alibaba ecosystem.

Hang discusses the thinking behind designing Qoder, how it differentiates itself from peers currently available on the market, the future of agentic work, his fears and excitement about the pursuit of AGI, and finally, challenges the notion that the future of AI may not be based on Transformers.

Christian walks us through Qoder’s business positioning, global ambitions, how it fits into the Alibaba ecosystem, and the reasons for routing between models, beyond just Qwen.

In today’s world, there’s no shortage of information. Knowledge is abundant, perspectives are everywhere. But true insight doesn’t come from access alone—it comes from differentiated understanding. It’s the ability to piece together scattered signals, cut through the noise and clutter, and form a clear, original perspective on a situation, a trend, a business, or a person. That’s what makes understanding powerful.

Every episode, I bring in a guest with a unique point of view on a critical matter, phenomenon, or business trend—someone who can help us see things differently.

For more information on the podcast series, see here.

Topics we covered:

Product

* Introduction to Qoder and AI Coding Agents

* The Transition from Copilot to Agentic AI

* The Future of Developer Productivity with AI

* Addressing Developer Bottlenecks

* Multi-Model Strategy and (Qwen) Integration

* Differentiated Views on AGI and AI’s Future

Business

* Understanding Qoder’s Positioning in the Market

* The Competitive Landscape of Coding Tools

* Qoder’s Role in Alibaba’s AI Strategy

* International Ambitions and Challenges

Transcript (AI-generated)

A. Hang Yu, Head of Product at Qoder

Grace Shao

And again, I just want to say thank you so much for joining me today, Hang and Christian. So today, the first half of our conversation will really focus on the product design of Qoder and the transition that we’re seeing from Copilot to Agentic. And then we’ll move into the second half of the conversation, which will really focus on the business strategy of Qoder, international expansion goals, objectives, and then how it really fits into the bigger Alibaba AI plan and the bigger AI playbook. So with that, I just want to bring in Hang. Hang, it’s lovely to meet you and thank you so much for joining us today. Let’s start with the very, very basics. What is Qoder in plain language and what does it actually do for developers day to day?

Hang

Hey, great. Thanks for having me. So yeah, so in one sentence, Qoder is an AI coding assistant that helps developers maintain and improve existing software system, not just build some new stuff from scratch. And I think that distinction is actually really important. So when we look at what developers actually do every day, we found that 95 % of professional developers spend their time maintaining what we call real software.

So commercially valuable, long-lived systems that are not building new projects every day from zero. So that’s why we design code around that reality. We are optimizing for the messy, important work of understanding existing code, making targeted improvements, and maintaining production systems.

Grace Shao

That’s really interesting. you know, there’s for me again, not a technical person. The hype we hear about is all this vibe coding. And then there’s even these marketing phrases calling it like ⁓ AI coding, build your app from a single prompt. That’s not what’s really happening, right? How does it actually really work?

Hang

Yeah, exactly. actually, that’s the flashy use case everyone talks about, like building one app from a single prompt. But if you’re working at a real company maintaining a five-year-old code base with 500,000 lines of code and 20 different developers who have touched it over time, you need help understanding what’s actually here. So you need help making careful modifications without breaking things.

That’s where code delivers value. So in terms of how we think about the AI developer relationship, we see it evolving through three stages. So the first is assistive programming. In this stage, AI helps the developer while human leads. So like code completion, fixing syntax errors. And the second stage is collaborative programming, like co-pilot. Like the human and AI works together like pair programming.

And the final stage is autonomous programming. In this stage, AI takes on complete tasks independently. So the developer can delegate work, and the AI runs in the background and comes back with results. That’s what our Qoder Quest mode does.

Grace Shao

So the really unique bit of Qoder Quest is the autonomous piece, right? And I really want to dig into that a bit more, a bit later. So right now I’m curious. So when you say the user experience is intuitive even for non-technical users, what design choices really led to that? Because honestly, a lot of developer tools are pretty intimidating, especially for people like myself who’ve never done any coding. But what I’m hearing on the street is people are going out of their way, even as non-technical people, building their own apps now with the help of AI. How are they able to do that?

Hang

Yeah, great question. So we have this philosophy. So don’t make users think about things they shouldn’t have to think about. So for example, if you look at some products, they have like 40 different AI models in a dropdown menu. Honestly, that creates a lot of cognitive load. So developers end up becoming model select or instead of focusing on building their own product. So our philosophy here is integrate the globally optimal models and give users the best results. So we will auto select the right model based on the task. So we believe model selection will be better than human selection. And the same thing with context management. The users shouldn’t have to manually figure out, OK, which files to include, what tokens to optimize. Our context engineering handles that automatically. So the goal here is to remove the cognitive overhead. And let developers focus on what they are trying to build, not on configuring the AI tool.

Grace Shao

I have a really dumb question, but is there a latency then between me prompting like, can you help me build this versus the machine telling me which model is optimized? Is there like a latency? It’s automatic.

Hang

No, no, there would be no latency. Yeah, it’s all automatically. The user will not feel it.

Grace Shao

that’s amazing. Okay, let’s talk about kind of the hype right now, the copilot versus the agentic transition right now in the coding space. Many tools are being called assistants or some are called copilots, You know, the cursors of the world. And cursors essentially been leading this. So where does coders autonomous capability really truly defer or is unique or different? And what’s really the big breakthrough we’ve seen here?

Hang

Yeah, this is the defining question, So Cursor has ⁓ perfected the co-pilot approach, real-time help while you’re coding. Their tab completion, tab, tab, tab, is industry-leading after two years of their custom model training. But I’ll say this. So Cursor’s tab completion compatibility is catching up fast. We have made significant progress in recent months and rapidly closing the gap.

But here’s where we see the real future, the autonomous programming. So you delegate a complete task, implement this feature, fixing this bug, et cetera, whatever. And then the AI works on it in the background. You don’t have to watch every line of code being written. You just come back and review the result. now sophisticated autonomous coding and production skill is still involved.

Hang

We think it’s about two to three years out, maybe 2027 or 2028, before it’s really mature. But that creates an opportunity window.

Grace Shao

That’s actually quite soon. So what does that really mean practically?

Hang

Yeah,so it means With autonomous coding you can actually Delicate your work and make and the the AI agentic will can work it background in the cloud so Like you can close your laptop and the AI keeps working

go to a meeting, go home, do whatever you want, the coder is still running. So in other words, you can spin up 10 parallel sessions working on different tasks, and it doesn’t slow down your own machine. As one colleague said, he said, I’m managing 10 agents. My productivity went up 10 times, and it didn’t mess up my work-life balance. So yeah.

Hang

So this cloud execution model is pretty similar to what cursor recently launched with their cloud agents feature. So both approaches let you handle your tasks to agent running remotely. The key advantage here is you are not tied to your machine. You can dedicate the work and then close your laptop and then come back to complete the result. And then when the

Hang

Yeah, and then when the agent finishes, we just need to review the results.

Grace Shao

So that’s actually my question. Like when you talk about reviewing the results, is it very obvious to kind of find the issues only in the result or do you have to go back to the process? Like how do you audit the whole process actually?

Hang

Yeah, so the agents will present to you summary of, this is what I’ve done. This is the result of the question, or this is the feature. You can see it through the browser, or the agents will submit a PR to your GitHub to your routing workflow. And you can easily check, OK, is it done good, or it needs to be modified again.

Grace Shao

So does that mean that junior developers in a way will be replaced then? Because essentially you only really need people who understand a higher level of the code and the execution can be actually outsourced so easily.

Hang

Hmm. Good question. So simple, agentic task works well today. You can already dedicate things like write a unit task for this function or add log into this module and come back get some good results.

So sophisticated, agentic coding at production scale, where AI can take a high level of business requirements and then autonomously design, implement, test, and deploy a complex feature across multiple systems, that’s still two to three years far away. So what this means for software development is a fundamental shift in how developers spend their time. ⁓

Hang

So right now, developers spend maybe, I think, 30 % of the time on creative problem solving and 30 % of the time on mechanical work, like writing border plates, fixing syntax errors, ⁓ updating documentation, writing tests, et cetera. But as agentic tools mature, that flips. So AI handles the mechanical work. Developers ⁓ can focus on what AI can do yet, like understanding what actually needs to be built.

Hang

making architectural decisions that require domain knowledge and validating that the code actually does what it should be.

Grace Shao

Mm-hmm. So in the end, it’s still like, there are still aspects of humanity that can’t be replaced by machines yet. Let’s get a bit more practical. I want to understand how precisely Qoder can actually turn a prompt or product spec into working code? What types of tasks or repos does it handle best today, right now?

Hang

So Qoder handles best what we call a bounded, well-defined task today. For example, implement authentication for our user login system or add relimits to our API endpoints or generate a dashboard for monitoring system health metrics. So these are tasks where requirements can be clearly specified, the scope is contained, and the success criteria are measurable.

Grace Shao

Mm-hmm.

Hang

But what’s harder today are tasks that require deep domain knowledge or ambiguous requirements like improved user engagement, that’s too vague, or like a required entire authentication system, that’s too large, that’s too large and risky for autonomous execution.

Grace Shao

So it seems like a lot of the tasks it can do is still pretty much something that’s very easily verifiable. It’s a bit more like a black and white kind of answer kind of task, but not so much things with nuance, right? I want to understand better. So, thinking about the developer workflow, and correct me if I’m wrong, there’s the planning stage, the code writing stage, the running tests, the debugging, version control, and deployment, right? Where does Qoder natively sit the strongest, like in the most helpful, and where do you hand off to other tools?

Hang

Great question. Let me break it down. So the first is plan and design stage, So Qoder is strong here through the spec generation. We can help the developer translate business requirements into technical specs. And the second stage is writing code. This is our core strings. The Qoder can write code across multiple files, handle complex logic, and generate boilerplate. And for the run and test stage,

Qoder can generate unit tests, integration tests, and run them either locally or in cloud sandbox. That’s building feature. And for debugging stage, Qoder can diagnose its errors from test results and fix them autonomously. But for production debugging with live user data, you still need the traditional tools. That needs to be taken care of. And as for version control or call out, we integrate with Git, GitHub, and GitLab. So a coder can create branches, commit changes, and create pull requests. But the actual code review and collaboration discussion happens in your existing tools, like GitHub, GitLab, whatever you use. But yeah, in the final stage, the deployment, we hand off here. So deployment involves your CI-CD pipelines, like infrastructure, monitoring system. So Qoder creates the code and the tests, but you own your deployment process.

Grace Shao

I see. I kind of want to take a step back and understand another big kind of hovering question a lot of people have right now, which is if we’re going to have agentic tools really implemented the work process, how do we understand developer productivity for the future? Will developer productivity still be measured the same way that it’s currently being measured?

Hang

Yeah, I think the fundamental shift here is this. So AI changes what developers spend their time on, not just how fast they work. So again, right now, developers spend maybe 30 % of the time on mechanical work, like writing boilerplate, debugging syntax errors, searching documentation, setting up environments. And only 30 % of the time goes to creative high-value stuff, like understanding what needs to be built. Like understanding what needs to be built, making architectural decisions, and validating that solution actually solves the problem. But with AI, agent AI flips that ratio. So AI handles the mechanical work. Developers focus on the part that actually requires human judgment. So when we measure productivity, we are not counting lines of code or tickets closed.

So we are looking at, can developers spend more time on higher value work? Can they ⁓ ship features faster without burning out? So what we are seeing is promising. 99 % of our paid users actively use agent model. Nearly 99 % of our paid users actively use agent mode. So it’s become core to their workflow.

80% renewal rate tells us that people see the real value here. And enterprise reports two to three times improvements in the deployment frequency. So the real metric here is simpler. Developers tell us that they are less frustrated. They are not stuck debugging environments or writing repetitive code. They are solving interesting problems. And that’s the productivity gain that matters.

Grace Shao

I see, I see. That’s really interesting because I think as a writer, when people really initially rejected AI, the idea was also that there was a lot of mistakes, AI slop, hallucination, whatnot. But then what people started realizing is that you can actually use it as a productivity tool. And like to your point, it doesn’t change how you think as a writer building out the framework, using your critical thinking and really still rely on your own creativity. But you know, you what you’re outsourcing is actually just like the execution that was a lot of the grunt work frankly. Right. Interesting, interesting.

Hang

Yeah, yeah, exactly. Yeah, you free up your hands and you, yeah, yeah, you free up your hands and focus on your head.

Grace Shao

Okay, so I wanted to ask another question on productivity and bottlenecks. So what is, I guess, one of the top bottlenecks or what are a few that you see currently in this space for developer right now? And I guess can we actually separate the separately answer this question, the first half being what are professional developers bottlenecks and what are kind of the new age, like casual developers bottlenecks and how are you helping these two differently?

Hang

Good one. Let me hit the main ones. So usually the bottlenecks, like the first big bottleneck is about the environment setup, So this is a huge time thing. The coders, and then the coder can help you set up the environment automatically, whether you are running it locally or in the cloud, pre-configured environments, automatic dependency resolution.

So you don’t have to spend two hours debugging your Python version complex or just trying to build your dev environments. And then for the flaky tests, coder can also detect flaky tests by running them multiple times and identifying inconsistency. So you can also suggest fix based on the failure patterns and the test outcomes. And then for...

For another bottleneck here is the legacy code. This is where the Ripple Wiki comes in. So remember how I said the documentation is always out of date for developers? Ripple Wiki uses... So Ripple Wiki delivers value here. So Ripple Wiki features here can use AI to generate up-to-date documentation from the code itself, plug the Git history. So it’s not just...

Here’s what the function does, documentation. But here’s a business logic and why it was architecture this way and what changed and why documentation. So the documentation itself stays fresh. When code changes, the documentation regenerates automatically. So we have measured about five times speed improvements, so from 60 minutes down to 12 minutes for team documentation.

And the last but not least bottleneck here is the context limit. So this is a technical challenge. Models have tokens limits, So we have our context engineering figures out what’s actually relevant to the task. So we don’t just dump the entire code base, which will crush your token limits. We intelligently select what the task needs and what the AI needs. and we ⁓ gain a better solution from the ⁓ context we select.

Grace Shao

That’s really interesting. So I think I want to talk about the models and orchestration here. So Qoder is a multi-model platform, right? You guys use Qwen, your in-house built models, but you also use other models like you mentioned earlier, use whatever is like kind of state of art model for the right task, right? How do you route between the models, thinking about latency, cost, evaluations, languages? Do you usually like have a preference for Qwen or the third party models when you’re assigning these models to tasks?

Hang

OK, so what we are trying to do here is integrate the globally optimal models. ⁓ So what we are trying to do here is to integrate the globally optimal models and give users the best results. We are not limited to just Alibaba’s model. So if one frontier model is better for a specific task, we use that. So like if GPT Excel somewher, we use GPT. If Qwen is the right fit, we use Qwen. So now, why does this matter? So first, it best has time. I’ll be direct. So Qwen model isn’t the best model in the world for every coding task yet, but it’s improving fast. So by using the best global models today, can serve users well while our own models can catch up. And if Qwen becomes the best model globally in your year, is our goal, then naturally we’ll use it more. And secondly, yes, sir. Yeah.

Grace Shao

But actually, just want to jump in on that. But actually, Curser is even using Qwen isn’t that fascinating that we just found out recently.

Hang

Yeah, so the cursor’s composter is... They didn’t officially admit it, it’s... So the community thinks they’re fine-tuned and post-trained based on Qwen or some Chinese open source model. Yeah. So first is the best time. And second, the cost optimization without compromising quality is our second goal. So not every task needs the most expensive frontier model. So simple completion is a smaller, faster model. But for a complex reasoning, we use the frontier model. And last but not least is about the reliability. So you don’t want your entire product to stop working because one API provider has an outage. Here we saw OpenAI has multi-day outage in 2023. we want to our reliability to our customer. Yeah, that’s why we use multi-modal strategy. Yeah.

Grace Shao

I see, I see. I was actually going to ask you on that. What’s the thinking behind designing the product using a multi-model design versus only proprietary Qwen? I guess you already answered that partially. ⁓ I also wanted to kind of double click on the fact that Alibaba is throwing 53 billion US dollars into AI infrastructure right now. know, isn’t it, is there not some pressure coming from up top to really hone in on Qwen or, know, like, I guess the question’s more about - Is Qoder really developing based on what’s best for the user and use whichever model that’s best for them? Or is it more focused on being part of an ecosystem, another tool out of the Alibaba set of tools that they provide? Does that make sense?

Hang

Yeah, great question. So it comes down to serving users best today while building towards to the future. look, Qwen models costs us like 1 fifth or 1 sixth of what Frontier model API costs. So that’s an 80 % cost reduction, right? That’s a structural advantage. But Qwen isn’t as good as the top Frontier models on context reasoning tasks.

That’s why we use a multi-modal setup at the balance cost, capability, and reliability. But the key in size is that this isn’t a permanent state. Qwen improves, as coding capability, sorry, as Qwen.

So the key inside is that this isn’t a permanent state. As Qwen improves, as its coding capabilities get stronger, the balance will shift. So we are not philosophically opposed to vertical integration. And we are actually pragmatically choosing what serves users best today while building towards our ownership tomorrow. So for enterprise customers in China, components actually require a domestic model anyway. But for international customers, they often trust well-known frontier models or GPT models. So multi-models board let us serve both.

Grace Shao

I think that’s a really interesting business strategy because end of the day, to your point, it’s a very pragmatic approach to actually serve your customers best and to serve your customerbest, you actually get more business. That’s just how it actually the virtuous cycle works, right? Instead of building up these guardrails and the paywalls. I want to kind of pivot to strategy soon, but before we do that, I really want to ask you a few other questions just on more, a more broad general question. One is, you’ve been working in this space for a long time. There’s a lot of hype around Agentic tools, not just a Agentic tools in coding, right? How do you view a Agentic AI in the next 12 to 18 months? And how do you think it will actually affect what even the general mass think of AI use AI?

Hang

I think agents will swallow the whole market. ⁓ People will use more more agents in their daily workflow. So when you’re trying to use a chatbot, you need to copy paste a lot of data, your contacts, your own data, your domain knowledge.

To chatbot and then generates some specific task results. But by using agent, agentic AI, you don’t need to do that. The agentic AI will live in your workflow. It knows and it contains your domain knowledge. And it knows your context by nature. So this means agentic AI knows ⁓ what you’re trying to do, what you did.

What you are trying to do and what you want. That’s actually a big difference between the big difference between the LLM and the agent AI. So I think, yeah, yeah.

Grace Shao

I see. I think that that leads me to the next question, is like, so for teams who are customizing tools or agents, like you just said, they would have domain knowledge. They already know your work processing really well. And how does it actually work? What’s the plug-in API story with Qoder in this sense again?

Hang

Yeah, so this is about the domain knowledge question and the accessibility work. Let me break down how the domain knowledge and the accessibility works. So first, we have MCP supports. We support the modal context protocol, which is becoming the industrial standard for connecting AI agents to external tools and data source. So this means the coder can ⁓

integrated with thousands of tools in the ecosystem, database, API, version control, and project management through a standardized interface. And then we also support subagents and skills. So teams can create their own specialized subagents. Think of this as task-specific AI workers with their own contacts and capabilities. So ⁓ you want a code review subagents turned into a

So you want a code review, sub-agents turn to your team standard. You can define it. You need a security scanning sub-agents for your compilers requirements, build it by your own. So these are the version controlled and shareable across the whole team, and can also run in parallel. And as for the domain knowledge, you can inject your own documentation, design system guidelines, and your coding standards directly into coders context.

And when Qoder generates code, it will follow your rules automatically. So now, ⁓ here’s the key part. How you actually use all this. So you can think of Qoder like a Stripe for payments or Tailor for communications. You just embed it into your existing development workflow. You don’t need to replace your IDE. You integrate Qoder into VS Code, into JetBrains, into Temrano, into your CI-CD pipelines, whatever and wherever you already work. And there’s actually a precedent here. So Alibaba’s Model Studio platform has enabled over 800,000 custom agents across different domains. And we are also trying to bring the same extensibility here to coding workflows.

Grace Shao

That’s really really interesting. Thank you so much for sharing all that and I do apologize if any of the bit that I didn’t like sound that smart because it’s so technical but I really appreciate you breaking it down for me and really explaining it to me in very simple language. I have one last question for you which is something I always ask all my guests. ⁓ What is one differentiated view you hold? And this doesn’t have to be about work. It could be. It could be about your space could be about agenda coding. It could also be about anything else in the world. It could be about your experience in Silicon Valley versus China. Just a differentiated opinion review of something that you think is not that mainstream or it be a bit against consensus.

Hang

So we talk about something related to agentic coding. actually, I don’t think the transformer-based LLMs in the future. Because I actually do agree with the point that LLMs is just a compressor of the tags. They don’t really understand what they are talking about.

But I think as the develops, as the technology goes, one day we will have the truly AGI. But it’s not based on Transformer.

Grace Shao

What does AGI mean then?

Hang

AI means the AI can really understand what it’s talking, what it’s doing, and really have emotions. Yeah.

Grace Shao

Fully human-like, emotionally, intellectually functioning being.

Hang

Yeah, like human are…From this perspective, they have emotions, have logic, they understand the work, the 3D work, and they have logic of, okay, why I’m doing this, and what I’m going to do in the next.

Grace Shao

Is that scary to think? Because in many ways, they would be more powerful than us, right? They can speak every single language on Earth. They will know more knowledge than us as an individual. But can we though? What if, you know, what if there’s charge and they can charge themselves these days, these humanoid robotics?

Hang

we can cut off the power. You mean to take off and charge by themselves?

Grace Shao

Yeah, like, do you ever worry about this AGI achievement going rogue?

Hang

To be honest, I’m both a bit excited about this and a bit scary.

Grace Shao

Yeah, I think especially for the next generations, right? Like what does it mean for them? Yeah. Thank you so much for your time. I really appreciate it.

B. Christian Hu, Head of Global Marketing and Operations at Qoder

Grace Shao

Christian, thank you so much for joining us. I just spoke to your colleague, Yu Hang. he was super helpful in explaining to me the technicalities of Qoder and the design of the product. My interest right now is really shifting towards the strategy and business side of Qoder. I’m really glad that you can join us today. So why don’t we start with the beginning? Why build Qoder inside of Alibaba? What was the thinking behind that? And what unique advantages does that really give you?

Given that you have someone who just threw $53 billion into AI infrastructure that’s backing you, right? In terms of distribution, your infrastructure, your data, your model access, how does that really advantage you in many ways?

Christian

Okay. Thank you. Thank you, Grace. Thanks for having me here. You know, Alibaba has a very big plan and has very big ambition for AI ⁓ and his position in the future in the AI industry. And as you know, Alibaba has a full stack. We call the full step strategy from the cloud to model and to application. So that’s the full stack strategy. And for, ⁓ you know, a home computer, you know, we don’t build Qoder in a vacuum. Yeah, we build Qoder from the real context because we can find, we have got so many, maybe thousands of engineers from inside Alibaba. They are facing very real software problems in real software development. They have many issues in interface and how to fix the issues. But for the existing AI coding tools, they are maybe reactive. cannot...resolve some existing problems, maybe some complex and sophisticated problems in the real software development process. So they are trying to refer to a new product. we build Qoder just from inside. We have so many inside, the mind from the inside engineers. also, as you know, be part of Alibaba give us, mean, have so many advantage because first of all, we own models.

Because you know, you know, we have early access to Qwen and that’s how I’d propose large-scale models We don’t just use Qwen we also reinvent and feed the Qwen with our real data from our AI coding Qoder base. So we just really reinvent the Qwen and we are coordinated with the Qwen team to improve the performance of Qwen models and

Secondly, we own the cloud. I mean, the bottom layer of Alibaba’s AI strategy. that’s the cloud. Every digital tools and maybe even every AI coding tools should be based on large cloud infrastructures, consume very large computing powers. So cloud is also a very important factor in AI coding platforms.

The third, think we, I think the most, last but not least, we also have the advantage of Alibaba has so many enterprise customers and so many business, you know, we got some business lines maybe from the consumer to the from the SMB to large enterprises, from the consumer to industries. So we got so many data across, as real data for us to, how to do involve the, the area AI to, you know, to ⁓ upgrade our coding platforms from the real data. that’s the, so, ⁓ so back to the, the origins of the Qoder So we just not, you know, we’re just not create another AI coding platform just for the tool, which we want to build a Qoder as a new platform for a Agentic platform for the real software. We want to build the Qoder for real software, not just for fun, not just for the fun making, but for the real software.

Grace Shao

Thank you, that’s really helpful for everyone to understand. I think saying we have a model is the most humble thing anyone can refer to Qwen as, because you guys have one of the best leading open source, open weight models in the world right now. Actually, so on that right now, you know, we’ve been hearing a lot of news about people adopting Quinn globally, and it’s really being used not just in China right now. ⁓ Who are the people using Qoder actually?

Are they mostly Chinese developers or are you guys actually expanding globally? That’s, I guess, the first half of that. And the other half is, are these mostly professionals or are they like students or are they enterprises? Like, how do we understand the demographic here?

Christian

Yeah, good question. We are building Qoder for global developers from day one. Yeah, so that’s not just for Chinese developers, but for global ones. And for now, ⁓ actually for now, our users are mainly come from the, we call them individual developers, not just the enterprise users because know we the enterprise editions on it our way we are just we want to start with the individual you just at the first and and the funding you know geographically from China to the overseas market so we want to some difference between you know preference different preference for different you’re just in different regions yeah from in China they would prefer to the more, more integrated coding system existing with the existing customers. It may be fully integrated with existing systems. And in the Western countries, maybe in some in US or maybe some in ⁓ Malaysia, the users will prefer to more flexible workways. So they’d have a different preference for different coding languages. And for now, we are trying to solve the real software development problems. the most of the users come from the professional users. Because they have a real problem to resolve in the real software development process. Because they doing their work. Because they doing their work and they want to increase their productivity levels to solve more problems. So the main users come from the professional users.

But we also find the increasing adoption of the individuals and some new learners, maybe some product designers, maybe some UI, maybe some UX designers, they want to deploy the AI coding tools to to expand some ⁓ new interface or maybe some new mini apps to increase their productivity is, yeah, so that’s the trend. we believe, uh, Qoder is created to solve the real problem real software development problems. But we also find that the, our agentic, uh, models and the questing models can allow, uh, more users, just like the new learners and new indies, new learners to, uh, new individuals to use our software to create something new, something more powerful. Yeah.

Grace Shao

So my understanding is that Qoder itself came out of the desire to help professional developers. But Qoder Quest, that mode you can go into, can actually help people with less technical backgrounds to be able to play around with it and potentially still build their own thing.

Christian

Yes, Quest Mode is maybe the key to unlock more space for web coding. What is web coding? Web coding is for non-professionals to use the Qoder tools to create something new. I think the Quest Mode may be the new way to unlock more web coding. But our Quest Mode is different from the existing agent model of other competitors or maybe other coding platforms because our Quest Mode is always is really is also a Agentic is a Agentic about also ⁓ we called a delegated and you can control the workflow and control the result on the question model. So that’s that’s for real software. Yeah, so the Quest Mode of coding is also is for real software.

Grace Shao

I see. I think I have a question for you. I asked Yu Hang earlier as well. But basically, my question was that, you know, there are a lot of tools out there already, like the co pilots Curser co whisper Warp you know, there’s there’s a whole array of them. How do I understand where Qoder sits so Yu Hang explained it to me in the technical sense of where it sets between co pilot and a agentic. Can you explain to me where Qoder sits in terms of like the business positioning.

Christian

Okay, we have a slogan for a Qoder We Qoder a agentic platform, a agentic coding platform for real software. So we got two keywords, Agentic and the real software. I just explained what is the software. I can explain more about the software because many developers, mean, no matter professional developers or non-professional developers, they found something critical in their developing process because they need to know the existing Qoder base. They need to understand what the existing Qoder means, what the existing documents mean for the codes. So they need to understand so many documents, so many files to understand what the coding process will be like.

So we created a Ripple Wiki and a non-context memory function to understand the codes, understand the documents, understand the behaviors of the developers. So for the new entrants, for the new developers, they know how to get the real software down. So that’s the issue in real software development context.

Yeah, that maybe seems different from what we call the rubber coating. So that’s for real software. And for Agentic, for most coding platforms, we call it your prompt and the Agent, the React. for Qoder we call it your delegate, then Qoder delivers. So then Qoder that deliver the real software and real results and the real apps for your delegations. So that’s the difference from the existing, I mean, maybe some most of the competitors to agentic platforms. So we are just not want to respond because of the delegation that can, you know, to free you from the desk. mean, for agent, for most agents, you also need to speak to the agent.

You need to communicate with the agent while you are sitting alongside the desk. But the dedication can free you. You just don’t need to communicate with the agent. don’t need to interact with it for line by line, word by word. You just need to monitor the per science and maybe some need to check with the workflow so that the Qoder can deliver the result. That’s the difference between way from the other competitors.

Grace Shao

Actually, let’s take a step back. I’ve noticed that when I was doing some research on you guys that, you’re, it’s not just Alibaba that is creating these coding tools. You know, we have even just in China, we have ByteDance creating something similar. Obviously, Microsoft has things like has Copilot. So then we are looking at obviously then the startups and the Frontier Labs all kind of swarming into coding tools. Why is that? What is really the reason for focusing on coding right now as the next kind of use case?

Christian

Yes, actually, coding, as we just talked, coding may be acceptable for every engineer, maybe a non-engineer, maybe you’re just a digital learner, maybe just an analyst for the AI. So coding is the most certain way for token consuming, for the ⁓ infrastructure consuming, the tool to the cloud infrastructure. Maybe the coding is the most important way. And for different players, they are playing different games. For giants, you just mentioned the Bydance, maybe Microsoft, maybe AWS, they have very large cloud infrastructure. They just to integrate the new AI application to the infrastructure. And for SRAPAC, maybe some other front-end labs, are... ⁓

They are trying to find the new path to the developers, maybe some to the application levels. for Cursor, they are just a new service. They are deploying the large-scale models to make it accessible to the new developers. Cursor has...

I think for new startups, have their weakness, maybe they have their cost structure weakness. mean, they are the users of the large-scale algorithm. They can’t dominate the large-scale algorithm model and maybe they will be at risk to disengage with the large-scale algorithm model owners. So that’s the cost structure that is at risk for them.

And for Qoder know, as I just mentioned, the Qoder is born within Alibaba. Alibaba has full stack from ⁓ cloud infrastructure to model to application. So I think that’s a great advantage. As you know, ⁓ as you just mentioned, Qwen is very popular around the world. some, you know, I just kind of found the news that Airbnb, Airbnb,

Grace Shao

Yeah, Brian Chesky

Christian

Yeah, yes, I said adaptedQwen open source model to the real invent their own infrastructure That’s very popular and for it for cursor. Yeah, it’s just anything for a cursor itself They just came up with a new composer composer model because it’s a small small model for coding but We have a speculation that the this small model comes from the Qwen or maybe some cheap. Both of them are from China. So we will find that the full integration and full connection with the model and the client structure will benefit a for Qoder in the future.

Grace Shao

Actually, on the point of how Qoder sits in Alibaba, can we kind of zoom in on that? Can you help us understand the big picture? Where does Qoder sit in the Alibaba Grand AI strategy across the stack?

Christian

Yeah, I think Alibaba AI strategy is a very big picture and also very long roadmap for what we call the super artificial intelligence. We call it super artificial AI. Yes, wait, wait, wait, ASI, that’s the roadmap. so from the map, so when we look at the map, I think it will form the bottom to the top level.

I just mentioned is the cloud infrastructure. Yeah. The middle level, mean, the Qwen, maybe some other large Niagara models in the middle layer. And the Qoder, I think it sits on the application level alongside with some other applications. Maybe, you know, Dink Talk, maybe Quark, maybe some other applications on the application levels. think that’s the full, we call, so we call the full stack strategy for Alibaba in the near future. Yeah.

Grace Shao

I understand you lead Qoder, international operations and marketing, right? And that’s a pretty big title you got here. What’s your GTM focus right now? Where are you kind of focusing on essentially selling your product to? Like which markets, through what channels?

Christian

Yes.

Grace Shao

How is this kind of working out for you?

Christian

Yeah, so I think it’s a tough task. I know because we are new, we’re just a baby. We just launched our products about two months, no longer than three months. We’re just a new one in the market. our ambition is here. We want to ⁓ become the top global coding platform for global users.

So for me, think the go-to-market strategy for ⁓ us to go global, think we need more partnerships, we need more integration with the local communities. So for me, that’s why I’ve followed to different regions to meet up with local community developers, maybe the leaders of local communities. We want to talk about it.

What kind of preference for them, what kind of products they want to prefer in their context, in their context, maybe within the enterprise, maybe within the individual developing context. And also we are aiming to the global products, we are not going to separate.

China with the global markets. We want to offer two different products. We just offering one product, one platform, one user interface for all the users around the global. So that’s the challenge. But we believe we can do that because we are trying to interact directly or maybe talk directly with ⁓ the developers from the different corners of the world.

Grace Shao

Yeah, that’s definitely quite different from how Chinese companies used to sell their products abroad, right? Because it used to always be a one app location or one interface, domestic one interface for the globe, for the rest of the world. So actually on that, think my final question for you really is just how are you navigating the current climate? Obviously it’s not.

Probably not easy given just current situation with geopolitics, with the competition, with everything, right? How are you navigating differences between China and the global markets in terms of adoption, compliance, data residency requirements, and even developer culture? Because I know you were just in Singapore, like you mentioned, you’re flying around a lot, you’re meeting people from different parts of the world. How are you making it all work?

Christian

Mm-hmm. Mm-hmm. Yes.

Quite different. I think it’s quite different. I have seen huge difference in the developers in China from the developers in Southeast Asia. Because I just flew to Singapore and met up with some local developers. The difference comes from different angles.

User interface and the language and maybe some other features of the software. So the difference, is huge difference between the different rappers geographically and demographically. Yeah, so all the differences are very, very huge. And we also find some, because we are trying to offer our enterprise edition in the near future. So we also started a very

We have made a study for the enterprise levels in different markets. We find quite different stories because in China, the developers in enterprise, maybe in the small companies, in large small companies, they would prefer the company to purchase the software. They prefer to use the existing software. The truth is using software can be embedded into the existing OA system or existing software ecosystem. So they prefer the existing, they prefer the integrations. But in other countries, maybe in other markets, they prefer more flexible and more athletic applications. They don’t care about the integration, they don’t care about the...

I mean the sophisticated integrals, maybe sophisticated workflow and controlled by the company owners. So that’s the different culture. So you can find it’s very common for individual users to buy our software, buy our products in other countries, mean outside China. Yeah, so that’s the huge, that’s the huge difference.

Grace Shao

That’s really interesting. I would have thought that integration part is something that everyone kind of wanted because it makes your workflow so much more seamless, right? And that was the selling point for all of Microsoft’s tools essentially, right? In the software era. Interesting. Yeah.

Christian

Yes. But it doesn’t mean that we don’t need to make an integration or some ecosystem with the... You just mentioned Microsoft, mean, the OA or maybe some Workday, some other software platforms. We also want to explore more connection with existing software.

Grace Shao

Thank you so much, Christian. I really appreciate your time. Thank you.

Christian

Thank you. Thank you so much.

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“Retail is simple. Retail is just how do you sell something, and make someone’s eye light up. AI or any technology you add to it, is just another way to do that,”

— Sharon Gai, retail tech and AI expert, former Alibaba executive.

Joining me today is Sharon Gai, an expert in AI and innovation, with a focus on retail. She was an executive at Alibaba, where she advised brands and heads of state in crafting their digital strategy with programmatic marketing and AI.

In this conversation, Sharon shares her journey from working at Alibaba to becoming a consultant in AI technology for global companies. She discusses her experiences in e-commerce, particularly the evolution of live streaming and innovative marketing strategies in China. Sharon emphasizes the importance of AI integration in retail operations and the future of shopping with AI avatars. The conversation concludes with insights on simplifying retail to focus on core selling principles.

Sharon was selected as a RETHINK Retail’s Top Retail Expert and a LinkedIn Community Top Voice in 2024. She has two books, E-commerce Reimagined and How to Do More with Less Using AI. For more of her work, go to sharongai.com.

In today’s world, there’s no shortage of information. Knowledge is abundant, perspectives are everywhere. But true insight doesn’t come from access alone—it comes from differentiated understanding. It’s the ability to piece together scattered signals, cut through the noise and clutter, and form a clear, original perspective on a situation, a trend, a business, or a person. That’s what makes understanding powerful.

Every episode, I bring in a guest with a unique point of view on a critical matter, phenomenon, or business trend—someone who can help us see things differently.

For more information on the podcast series, see here.

Chapters

02:27 Experiences at Alibaba: The Global Leadership Program

05:11 E-Commerce Evolution: Insights from Tmall and Live Streaming

07:54 Innovative Marketing Strategies in Chinese E-Commerce

10:23 The Rise of Live Streaming in E-Commerce

31:33 The Evolution of AI Avatars in Retail

34:09 The Impact of AI on Shopping Experiences

41:00 Challenges in Retail During the AI Revolution

43:22 Integrating AI into Retail Operations

48:43 The Simplicity of Retail: A Unique Perspective

Transcript [AI generated]

Grace Shao (00:00)

Hey Sharon, thank you so much for joining us today. It’s great to reconnect with you. We met like, I think four or five years ago back in Shanghai and you were still with Alibaba, right? So why don’t you start with telling us about your journey? Like you grew up in Canada, you worked in Hangzhou, Alibaba. I know now you live in New York. How does it like, how did you kind of bring together all those expertise to what you do today, which is help?

Sharon Gai (00:27)

Sure. So when we met, I was working at Alibaba still. For me, as somebody who was born in China and raised in North America, and then I chose specifically to go back to China to work for a bit, the reason why I did what I did is I just knew that going forward 10, 20 years out, the two major superpowers would be the US and China. And I already had a pretty good understanding of things that were going on in North America.

⁓ Going back to where I was born and getting the chance to work there at one of the tech companies there really opened my eyes to how both countries work. I think in the future it’s going to be a ping ponging back and forth of I’m sure different ⁓ globalized companies and projects between the two places.

⁓ And so that had blended pretty well to what I do now, which is a lot of writing and keynote speaking and consulting for global companies that both have a footing in China and the U.S. So it all ties together pretty well now, but definitely as I was going through my through line or life trajectory, it seemed very confusing in the beginning phases.

Grace Shao (01:35)

Yeah, tell us a bit more about your time at Alibaba, because I know you were part of quite a special cohort. was a of a test and trial group of international cadets, per se.

Sharon Gai (01:47)

I’ve never been in cadets before. But I guess with anyone joining Alibaba, it feels like going into entering some, a ⁓ corporate army of sorts. But the program was originally set up by, or it was a brainchild of Jack Ma’s. He had always, I think he had similar thoughts, which was, you know, eventually you’ll hit the ⁓ bottleneck of about a billion or so. ⁓

Sharon Gai (02:09)

internet users in China, where do you then grow the company beyond the billion users? You have to find it outside of China. And so the first place of external search was Southeast Asia and then into the Middle East, Africa, Europe, and then eventually the US. And so his long-term vision was

to recruit people who came from those places, those corners of the earth, to get them to come to China to be in green with Alibaba’s culture, a way of working, and to bring them back out again, and then ping pong back and forth, just as I thought. So I think his vision and my own personal vision aligned pretty well. So that’s what got me to join the program. And yes, it was definitely an experiment. There were many, what I would call seasons of us or cohorts.

every single year there were new people that came in, from different cultures based on the strategy of the company at the time. think in certain years, they really wanted more, a certain language to be spoken. So they, really hired for that specific language. and it, definitely changed at different versions. but the idea was to bring in.

people who are bicultural, multicultural to eventually lead some of the business units that was trying to expand outside of...

Grace Shao (03:22)

And actually on that point, what were you doing at Alibaba? I believe you were involved with Tmall, right? So the international business, flagship business of Alibaba’s e-commerce sides. Could you tell us a bit more about that?

Sharon Gai (03:34)

So the first sort of business unit I was in was called Tmall Global. Our larger BU was called Tmall Import Export. of course, as you would know, Chinese tech companies always like to change names and just change things. Embracing change is one of the values.

So at first it was called Tmall, import and export. And I was first on the import side. And then I went to the export side, which is what we call Tabout Tmall world, where there’s about 50 million Chinese diaspora around the world. And they also will use Tabout as a shopping app. At the time was also the growing footprint of the Shiians and Tmus of the world, where these local Chinese e-commerce apps were trying to leave China.

And so Taobao Tmall World was also part of that exercise. And so those were my main two. the first was, or sorry, one of them was Taobao Tmall Import-Export. And then I moved to Tmall Classic, which is the domestic side of Tmall.

where the brands, most brands were either Western brands selling into China or Chinese brands selling to local Chinese consumers.

Grace Shao (04:47)

So you’ve really got a good look into how the retail e-commerce digital evolution happened in China. You’re super plugged in. And you were there for like six, seven years, right? So when you left, you published a book, you published E-Commerce Reimagined. And I believe at that time it was COVID, pre-COVID, and just everything kind of changed again. And we saw the rise of e-commerce really.

⁓ being part of the daily lives of North American consumers as well. Tell us a bit about like, I guess, first your book and then tell us about what you witnessed over the 10 decades, sorry, about six or seven years while you were Alibaba.

Sharon Gai (05:25)

Yeah, so in 2017 when I joined, was the height of live streaming starting in China. When I joined T-Mall Classic, I was actually one of the first teams to set up a live stream and take it to the US. So funny today, I’m back in, funny today I’m in Kuala Lumpur, Malaysia where I’m joining you for this podcast, where Jackson Wang had a concert here yesterday.

And he was one of the celebrities that we collaborated with first to do one of those live streams. At the time when we were trying out and testing out live stream rooms, we didn’t know about the flow, how to direct things, what questions to ask him, how do you showcase the products in a very natural, organic way. All of those were tests and experiments that we figured out throughout the process.

But during that time, I mean, in a lot of, so I do a lot of keynote speaking today and in a lot of the keynotes that I do, I always start off with comparing just the size of the consumer economy of China, where it is the largest one in the world. It has the most number of internet users. It also just has a very voracious consumption habit. It also has the highest,

internet penetration in the world, the number of mobile users in the world, and people, and out of those users, people who are buying things online. There’s a high amount of trust online because historically from Tmall, from JD, there’s been very, very high standards by merchants. So when merchants enter a marketplace, there’s usually very demanding.

terms for them to host returns, be able to accept returns, to deliver things on time. And that standardization eventually increases trust in the marketplace that even if there is a new seller, new tab out seller that emerges, the consumer will most likely trust them versus if you had that same transaction happen in the US, there’s a lot less trust in the marketplace.

So 2017, 2018, we’re laying out all of these foundations. And I think what I took away is the immense competitiveness of that space. And so out of competition, naturally there’s more innovation because as a merchant, you’re fighting for the same eyeball that your competitor is. So either you’re going to lower your price or

better your brand or better your quality. There’s some sort of lever that you have to tweak to be better than your competitor. I’m sure you’ve heard the term involution. In China, it’s an involutionary environment in schools, in academia, and definitely in business. And so from a brand’s perspective, there’s just a lot of...

⁓ playful and competitive things that they can do and that’s definitely

Grace Shao (08:20)

Can you give us some examples?

Like what kind of playful gimmicks or tricks would people have to use to kind of draw more eyeball over?

Sharon Gai (08:28)

So back then I was part of ⁓ the Tmall food team and I was in category management at the time. And this is a traditional sort of key account structure in all sorts of countries or categories where your top accounts will produce roughly 70 to 80 % of your total GMB.

And then the next tier, mid-tier type of brands will produce about 10%. And then the long tail produce another roughly 10 % or so. And so in...

Grace Shao (09:01)

So is that just in China for Taobao and Alibaba? Is that like a reflection of the whole industry similar kind of to how the marketplace play out in the US?

Sharon Gai (09:10)

It’s similar in the US for sure. If you look at the large FMCG companies, Procter & Gamble, Unilever and Nestle, the large three will take up about 70 to 80 % of the market share. And then depending on the subcategory or the category that they’re in, they will pretty much dominate the market or they’ll dictate a lot of how the market shakes out.

Grace Shao (09:12)

Okay.

Sharon Gai (09:36)

So during that time, or let me preface by saying that’s what you see in a traditional offline heavy space. So in a supermarket, for instance, where things are a lot more traditional. So in 2017, 2018, around that time, it was when online, there was that huge push into online. And in the...

food space, there was a lot of DTC brands that sprung out of that. At the time, China didn’t have that green light, red light policy. So the consumption sector was really robust. lot of consumption startups, FMCG related startups raise a lot of VC dollars and wanted to IPO. So at the time with their VC dollars in their hands, they were able to create all sorts of new brands.

So the Chagees, the Nice Nose, these sort of tea brands and bakery brands that you see often in Southeast Asia or in China, those were all sort of created around that time, 2017, 2018 time. And I remember...

going back to your earlier question, so what sort of creative things did I see? There was this milk brand that experimented with adopting cows. So in China, depending on where your listeners are, the dairy industry is very traditional. And in China, there’s Meng Niu and Yili are sort of the two very, very archaic, hundred-some-year-old companies.

been in China just for so many years and everyone knows them. And around that time, would not really do much in the e-commerce space because they knew they had that foothold in the market. And so out came this new brand called, in Chinese it’s called Zhinyang Yitou Niu, which translated is Adopt a Cow. Funny name, and I think part of their success was also

the cleverness in developing that name. I wrote about this case study in my book too. Their goal or the goal of their founder was traceability. So a couple of years earlier than that, there was this huge milk powder incident where several babies, I think infants died because of this milk powder was poorly made. And so his...

Focus was could we have every family in China quote-unquote own a cow that they can milk from afar? And that when they get their milk delivery, it’s from that cow that they’ve raised or that quote-unquote they adopted So that was his ultimate vision That’s how that’s how the name came about

⁓ and the things that we did was, selling milk cards. we, so, so traditionally on an e-commerce, have skews where you’re buying the actual product. This was the first time where we’re buying, a, almost a gift card, but this card would just be for this one brand and it would be sold at a much steeper discount. so.

That’s why on the consumer side, you would want to buy it. On the merchant side, you would want to sell it. And we would roll this product out to a lot of other categories for a lot of other products where people knew they would be spending that amount of GMV. So anything like toilet paper, rice, flour, milk, health supplements, any sort of category where you knew you were gonna spend that much.

in a year’s time for you yourself and your family because it’s just one of those products that you frequently shop for. And so that was something that eventually the entire platform rolled out to the entire team all rolled out, but it started with that one company. And the reason why that company did that was because there were two very large incumbents.

And the only way that he could compete is if he was innovative enough to think of something else. And throughout my time, I worked very closely with that team to see them from day one where they had two followers on their store to now if you go and look at that brand, it’s the millions of followers they have. think by now they’ve also IPO’d because they were able to IPO before the red light, green light policy.

and they set a new standard because, out from out of that, there was a new type of, DTC, all sort of mindset that came out of T-Mall where, you know, if you were a no-name brand, if nobody knew who you were, as long as you were able to think of attractive enough and innovative enough things to keep attention and to keep your consumers.

to for you to for first of all your customers to discover you and then for them to come back again and again you you were able to Survive even if you were a newcomer and even if there were very large established incumbents in your category and so that was a slice of what I learned and witnessed and there was many many more examples, but

It’s a that one’s a pretty notable and memorable one for me.

Grace Shao (14:47)

That’s like so interesting. have so many questions, but I don’t know if we’re gonna be able to double click on everything. Number one is, do you think it had anything to, this is like, we don’t have to go fully into this, but I do feel like 2017, 2018, like you said, it was a peak of like also private equity in China. And like, did you see consumer brands were just like going nuts? Like whether it’s like makeup, cosmetics, like FMB, definitely like a lot of brands peaked.

Sharon Gai (14:49)

No.

Grace Shao (15:12)

I’m surprised that this one’s still living, which is great, but it’s just like a lot of the makeup brands, like, you know, Perfect Diary also kind of did this, like they kind of just fizzled out, right? There was like these coffee shops all across the country that were coming up that were selling like specialized filtered coffee, whatnot, right? So it’s interesting to see that this company was able to utilize innovation in their marketing and branding, but actually sustain this business.

Number two, comment is how can they actually track which cow? It seems pretty crazy. Like surely they weren’t actually getting the milk from that specific cow, right?

Sharon Gai (15:49)

Which one should I answer? On the note of the cow, that was his vision. No, today, think, well, I also stopped following the brand after I left the company. To my knowledge, it did not go to that extent. However, the extent that it did go to was the founder did do a lot more beyond just the cart.

Grace Shao (15:53)

Yeah, so it’s not actually like played out, right?

Sharon Gai (16:14)

He also wanted to and I’m sure you also probably covered a lot of it 2017-2018 was a big push in agriculture from a Chinese government standpoint. He also wanted to create these he wanted to turn his factory into like a like a like a touristic activity where people could go visit the cat like your cow or you could go and

Grace Shao (16:35)

Okay.

Sharon Gai (16:36)

and experience how milk was made, how it was pasteurized, the entire process. It’s actually a very, very complicated process before it gets, know, we sometimes take for granted like packaged or milk from our fridge is taken and drink it, but it goes through so many steps. he, so within that sort of touristic factor, you could see,

certain cows were penned off and there was a video camera on each of them and each cow was numbered. So you could technically see it, like see your adopted cow. But to date, I don’t think it eventually, or that model stayed, but there were many, experiments that he did. And I’m sure there were...

Grace Shao (17:11)

Okay. Okay.

⁓

Okay, we’re going way off track.

session with these cows. I’m just like bring it back and I’m going to look up these cows afterwards. But it reminds me also this Alibaba thing where people try to gather points and they can own their own trees and they can plant a tree on Alipay. Right? Okay. So anyway, let’s bring it back. Okay. Let’s rewind and go back to retail and AI. and technology and innovation and retail. So live stream, you mentioned it, 2016, 2017.

Sharon Gai (17:37)

Mm-hmm. That’s happening.

me.

Grace Shao (17:51)

You’re one of the first, you know, part of the teams that were kind of, you know, really, I guess, leading the frontier of live streaming technology or even the strategy. It’s still not that mainstream in the West. So to start with, could you tell us what even is lives from e-commerce and how has live stream shopping really change in e-commerce in China? Is that something that you’re seeing? And like, I guess, ⁓

adoption in the US right now with TikTok or anything else.

Sharon Gai (18:22)

Mm-hmm. So live streams, e-commerce is when a seller and you can be a brand or you can literally be a person who owns something that you want to get rid of on the internet. You turn on your camera and you showcase this product live to an audience. And this audience could be some could be your actual followers or just be strangers. And then this sale is made technically the old in the old days on the in the western side.

The first adoption of live stream shopping was on Facebook live where Facebook live used to be a big thing. People would go live all the time. And then you host a room and then you make a bunch of sales and you’re actually recording a lot of the addresses and whoever bought you bought something by hand. It was very, very manual. And then on tab outside, they started with live streaming in app.

And in the US, think, you know, the Walmarts, Amazons tried their shot at live streaming. They invested a lot of dollars in creating these very posh and professional looking streaming rooms, setting up streaming studios. And all of that didn’t really go anywhere. So it had its spurt of interest. I think I think at the end of the day, it’s a it’s

One, it’s a timing thing and two, it’s sort of the way that it’s done. Yeah, and then I think the third, the host has something to do with it. So first timing in China, when I played with it, it was in 2017. That was still newish in China. China didn’t really take off with it. It didn’t really become mainstream until 2019-ish. That’s when we really understood. That’s when every single.

brand, merchant, you that, and if I’m outsourcing my operations to a TP, my TP better have a streaming room in their company. A ⁓ TP is a team all partner. if you don’t want to run your own e-commerce operations, you can outsource this to a company that knows this space very well. And really,

Grace Shao (20:12)

Sorry, TP is like a, a TP is. T-Mall partner, yes. And to give the audience, sorry to interrupt, but to give the audience some context, like how big are we talking about live stream e-commerce? Like give people like the headline numbers, like a leading influencer during Double Eleven or their annual GMV. What are we talking about?

Sharon Gai (20:40)

I think in total, I don’t know the exact numbers, but it’s north of a couple billion per year for sure for some streamers. Which is crazy.

Grace Shao (20:55)

which is crazy. This is like a single salesperson

if you think about it.

Sharon Gai (21:00)

Well, on camera they’re one person, but behind them is hundreds of people. And I’ve been through their product selection process. I’ve worked with them throughout the night. They do not sleep. Their streams start at 8 p.m. That’s not actually when they wake up. A streamer’s day is at 2 p.m.ish in the afternoon. They’ll wake up.

Grace Shao (21:03)

Yes.

Sharon Gai (21:21)

and they will start getting prepared. Their director will usually tell them today, is the, you know, we’re going through the final list. This is the final price that we’re gonna sell all these products at. At 8 p.m., usually 8 p.m., maybe sometimes six, sometimes nine, the show starts. You’re streaming for about four to five hours, so you usually finish at midnight-ish. And then midnight.

Starting midnight to around 5 a.m. You start to review what you just did because you have literally just talked for 45 four to five hours You sold a bunch of product products during double 11 It might be tons of millions of dollars that you’ve just sold per show And then tomorrow it’s gonna start again. So

You’re going to review everything that’s worked, that’s not worked, what you said was right or not right because some of these products repeat again the next day. So they’re always fixing their script and they’re always fixing how to say it better.

Grace Shao (22:15)

they buy the inventory or they take a cut from the brands? How does a business work?

Sharon Gai (22:19)

They don’t buy the

inventory. Streamers will not buy the inventory. The only fee that’s given to them from a per brand basis is a, is a, a, is is a, like a slot fee. So if I’m a brand and you’re the streamer, I’ll pay you $5,000 for you to show my product. But beyond that, you’ll also take a 10 to 15 % commission per product that you’re selling.

Grace Shao (22:48)

So this is very different

from how Instagram influence is kind of where my point is. Like for Instagram influence, it’s like, okay, you place this product here, that’s one off fee versus a live streamer in China is actually making money continuously as long as like people are buying through their link, right? And people sometimes don’t realize that.

Sharon Gai (23:07)

They don’t. Yeah. So this very traditional financial model for streamers in China is not the same in the US. In the US, it depends. Some streamers are just hired to talk for two, three hours at a time, and they’re almost paid on a per hour basis. Some streamers are paid.

They’re paid a set fee for the entire stream. It’s not even commission based. Their whole job is to, if they’re selling clothes, they just put it on, turn in front of the camera, comments will fly in and say, can you try this on another color? They take it off, put it on the other color. And so it’s almost very robotic and you sort of follow what the comments are saying. It’s not commission based. So they think less. They have to think less. They’re a lot less strategic also with placement of products. Which one goes first? Which one goes second?

because that also changes, that also impact GMB. So the two financial models are pretty different. But going back to your question earlier, is this happening in the US? It is absolutely happening in the US. It’s actually, the last numbers I checked was about $60 billion in the US, and that was last year’s number. So it should be a lot more now. The biggest US player is this app called Whatnot. I think that’s the most successful app.

They’re about as there. think they’re a series D startup now. They started with collectibles and I know some of their early collectible sellers. They sell trading cards. That’s the start of live streaming in the US is trading cards. Like there’s a huge fan base for these trade. There’s this Italian brand that makes this specific card and there’s a bunch of also intricacies with how to detect if a card is fake or if a card is real.

⁓ but, that’s the starter product is very niche.

Grace Shao (24:52)

So still quite niche. It’s not mainstream

and prolific in China right now, like how everyone knows what live stream e-commerce is, like any auntie on the street will know about it. So it’s just a very different kind of model. I wanna bring it back and talk about what that means. Does it mean it’s like...

shopping felt more personalized or did it mean like you said it was more rapid? Did it mean more interaction? Like I just want to see does that kind of foreshadow the future of technology and shopping experience?

Sharon Gai (25:23)

in the US? ⁓ I think it does. does. The way that I look at it is at the end of the day, you just want to be more, you want to reduce your barrier between the merchant and the buyer. The reason why live streaming works so well is because it’s live.

Sharon Gai (25:42)

Um, it’s so the, in some of the keynotes that I do, one slide that I have is the evolution of e-commerce. So in the nineties, you Craig list type listings, where it was just a product title and a description. There were not even photos because you didn’t have to put any photos. So some sellers were lazy and they just wrote down that I’m selling this leather jacket for $200. Um, and then Amazon came along and enforced the a plus content or, um, their, their product detail pages have to be.

put together in a certain way. So it became photos and then there were, was mandatory to have product videos. Then it was mandatory to, well, that part wasn’t mandatory, but then e-commerce became different influencers showcasing it. And then TikTok started Instagram, all of that. And so it’s pretty natural that things will just evolutionize. I think this goes back to competition.

which is China is just more competitive. So they jumped into live streaming a lot faster because we noticed that the merchants that live streamed did better than the ones that didn’t. It was as simple as that. And so the ones that didn’t would start doing it and they would have to, they were forced to learn it even if they didn’t really want to because that’s just what worked. And so in the U.S. it’s

it’s worked for these collectibles, toys companies. And I think other brands see that. now have, now what not has become cross category. Now it’s, they started with collectibles, but now a lot of fashion brands have jumped in, food brands have jumped in. Supplements also. And then in the TikTok shop world, it’s pretty, it’s more and more common for D to C brands to start streaming also.

⁓ to outsource their streaming to. Now there’s a new term called TTPs, which are TikTok Shop Partners, to do this for them.

Grace Shao (27:32)

I have a question. seems like in some ways that the China side, you said, the influencers, like not influencers, the live streamers themselves actually don’t need to be influencers. In many ways, it’s not that people go to them. It’s not like they build any, they bring any extra credibility. They’re just the avatar or, you know, the mannequin in many cases. Whereas in the U.S. it’s e-commerce is so heavily reliant on like the actual influencers, like, know, which celebrity endorses what.

you know, makes a big deal. Do you think we’ll continue to see that kind of divergent path? Or you think, you know, people will actually care less and less about who the influencer is?

Sharon Gai (28:12)

It’s actually kind of the reverse if we’re just talking about live streaming in that in the US, the people who are streaming are sort of no name people. And then in China, it’s the well known streamers, not the celebrities. I mean, on both sides of celebrities, let’s take out celebrities because they, ⁓ you know.

Sharon Gai (28:35)

They’re their own world. Also, some of them do

it sometimes. Yeah, there’s definitely pro, there’s definitely very well known live streamers like the Austin Lees of the world. They’re now a semi celebrity themselves because they’re so famous now. Like that type of profile is non-existent in the US where they’ve just gotten so good at the streaming side of things where they’ve in the branding side of things.

Grace Shao (28:53)

Mm-hmm.

Sharon Gai (28:59)

So will it diverge? think for the US at least, you’ll have more full-time B2C, more traditional Instagram influencers jump in and try it out. And it’s definitely gonna be a word of mouth type of thing.

⁓ like what I see on tech talk today is, there are certain influencers who, who are traditional influencers and now they’ve gotten in, they’ve jumped into the live streaming side. so I think in the U S it’s still a testing the waters type of thing. as for China, I think the big concern is the AI piece, where now if you go to any branded room,

If it’s an off hour, if it’s an off peak time, it’s some sort of AI avatar that’s streaming. To date, I’m sure you’ve heard of the Loyong Hall live stream room in 4.6.18. I think it was this year where he did the AI live stream and he sold X million of dollars. And it was more than he would do when he streamed himself.

in person as a human live. And that in the media space, there was a big wave of, are we going to be replaced by these AI streamers now? I think that was definitely a big, was definitely more of a PR push. Like it’s very easy to tweak those numbers so that you do make it seem like your AI version can sell a lot more. ⁓

Grace Shao (30:24)

Mm-hmm.

Sharon Gai (30:25)

You can play with pricing, the product that it sells, a lot of things. Yeah. So, but so in China, I think the future is going to be, are those big name streamers going to want to employ an AI version of themselves to conduct live streams? I think that is something that a lot of them are thinking about. Like once we let it happen, are we going, is it, how is that going to change the industry?

Grace Shao (30:50)

Actually, that’s where I really want to kind of take our conversation to next, which is like, we’ve talked about live streaming and that was, that’s kind of like the next technological, I guess, breakthrough in e-commerce for the US given that like, you know, Asia, East Asia is already like really, have, e-commerce already really, really, sorry, I want to say is like this live streaming model is already very prevalent across East Asia. ⁓

And like you said, the next stage of technological breakthrough for e-commerce in China or even including East Asia is really in avatars or AI avatars. And we already kind of saw Alibaba with their kind of like fake avatar, like these like cartoon avatars for a while now. Can you tell us a bit more about that? Like actually give us the context of like

how the AI avatar technology within Alibaba has evolved over the years and what we’re going to potentially see in the future. Because what you just said with the loyal health thing is essentially this frees up his actual free time, his time, and his avatar can just sell for him. Then do we still need him? Or, you know, is he going to become the brain behind it? And what’s the future of AI within this, this whole conversation?

Sharon Gai (31:57)

There’s I think there’s a difference to note between a real person creating an avatar of themselves and Being like the the Wizard of Oz the Oz and like puppeteering their avatar Versus I think what you were talking about is the Aya ease of the world where it’s like very futuristic looking She is a completely fictitious person person

Grace Shao (32:02)

Mm-hmm.

Yes.

Sharon Gai (32:23)

Her face is made up, the things that she wears is all made up. That type of, maybe for a better term, that type of sort of metaverse looking, futuristic looking thing is not so much an avatar. But it does happen. There’s also, Lil Mikaela is similar in Brazil.

Grace Shao (32:40)

Mm-mm.

Sharon Gai (32:47)

She’s also like, yeah, she’s like forever 19 years old. She’s worked with all sorts of major luxury brands. We’ll work with her now. So that model is proliferating around the world. I think that way of selling something or the evidence of that in the market was pretty big in...

the 2020-21 time of the world, it’s definitely died down a bit. To date, I have not seen new versions of Ayayi because technically if Ayayi worked really well, there should have been many, many versions of her. from a brand standpoint, we should also see, like each brand would have sort of their own version of Ayayi. think this brand called Florisys.

Grace Shao (33:22)

Right.

Sharon Gai (33:39)

which is this makeup brand. created one too, but she’s also kind of disappeared. So I think that that... Right. It’s like another look. Yeah. Yeah.

Grace Shao (33:46)

It’s almost like mascots, the mascots, right? He had to see like, right? Like, and they kind of just died and yeah, but people actually still want

a human looking thing, whatever, like a human being to try on the product. So to your point, like the Loi Yonghao example, it’s like people still like the fact that it looked like him, like a human, but it’s not him, right?

Sharon Gai (34:09)

Well, I think the Lui Yonghao specific one was just the novelty of it. yesterday, I was trying to buy something directly in Chachi BT because of their partnership with Etsy. I just wanted to see if it’ll get delivered, when it’ll get delivered versus if I just bought from a traditional dot com website. Like there’s a novel and I just came out of Capgemini.

panel a couple of days ago before that where every single panelist was like, we’re all shopping and try GPT right now. like, how, I don’t know how long this actually gonna last.

Grace Shao (34:42)

How good is

experience good? Because I can’t do it in Hong Kong. Is experience good? you... Is the whole...

Sharon Gai (34:47)

Yeah, you can definitely buy things within the app for sure. Not every single product.

Grace Shao (34:55)

But is it a good experience? But is it a good experience? how does it differ from going to a Alibaba.com versus like a Taobao.com versus like Amazon.com?

Sharon Gai (35:04)

I think it’s very dependent on if you’re very sure of this product because the weakness of ChadGBT and just shopping via LLMs is it’s way worse display. Because usually product photos are very beautiful and they hired models and background sets to picture this product very nicely. But in ChadGBT, a lot of these pictures are compressed.

Sharon Gai (35:29)

If they were originally long, they’re like short now. Like a lot of these photos are, there’s no standardization. And then also it cannot show videos. It can show YouTube videos inside a chain of chat, but there’s no, like if there was a product video, it can’t show the video. So if it’s something that you already know that you already buy all the time, then I think that’s, it’s.

Grace Shao (35:29)

Yeah.

Sharon Gai (35:51)

It’s easier to buy through an LLM, but if it’s something that you kind of want to browse and look at and maybe like flip through a couple of pages of reviews or photos, it’s not the best shopping experience. I would still go back to our traditional dot com website.

Grace Shao (36:04)

So basically like the agent will just go from your intent all the way to purchase all like in one go. You don’t have to click through a bunch of buttons. You just say, buy me this using this credit card go. Kind of thing. Okay. Actually tell us about more about that. Like are you seeing more brands that you’re working with right now adopt like AI within their purchasing process?

Sharon Gai (36:28)

⁓ From the perspective of a brand, I think all brands right now, the big to do for all brands right now is AEO, if you’ve heard of that term. So it’s a differentiation from the old SEO world where if I wanted to buy like a winter pea coat, I put it in Google, a bunch of blue links show up. I click through the Macy’s link, the Bloomingdale’s link, maybe another one.

Ralph Lauren or something. And I look at it and I browse through it, maybe at Dakar, maybe at Leave, I go to another site. That type of experience is being changed because now a lot of people are searching for their products through a ChachiBT, a Claude, a Perplexity. And so now as a brand, how do you not show up as the blue link, but how do you show up in the answer? So if I’m putting in the same query,

So I want to buy a winter p-coat and this is my budget and this is my type of style. I want it for this warmth because New York is usually zero to eight degrees in the winter. What are my choices? And then there will be choices that show up and some brands will show up and some brands won’t. So to get your link or to get that skew to show up, that’s called AEL or stands for answer engine or optimization.

Grace Shao (37:49)

And how do you get on that list? How do you make sure you’re on that list?

Sharon Gai (37:50)

There’s a lot of things you have to do. And it’s also something that all brands and also startups are trying to figure out right now. So it’s like an empty space for startups. in, give it another two quarters, you’ll see a lot of seed stage startups that do exactly this, which is an AO. ⁓

AEO product, like use me and I will make sure you show up in an answer engine. It’s not a sort of, a, or fortunately, it’s not a very straight cut thing. Like if you do X, then you will show up. There’s a lot of things you to do behind the scenes from just reorienting your product detail page to make it more crawlable.

Some websites want to wear off bots. Whenever you see the Cloudflare pop up where it says, check if you are a human. Most websites don’t want bots to crawl all over their site by default. And so you have to remove that. You have to also make sure that your product detail pages, or any web page rather, is very, very intricate. So much so that if anybody searched for a certain ⁓ question,

that whatever your product detail page says will pull up. So for instance, even with the example that I just said, a peacoat in the winter for New York, and it’s perfect for zero to eight degrees. That was part of my question. It’s very specific because the way that we search has just changed. We went from typing a couple of keywords to Google to now, even for yourself maybe when you look into China GPT or when you’re looking for an answer, your prompts are getting longer and longer.

Grace Shao (39:15)

It’s so specific.

Mm-hmm.

Sharon Gai (39:32)

just what you are looking for is more and more, because it’s easier and easier to find information that directly hits what we want to find. So in the zero to eight degree example, if one coat seller had that listed, would surface in the answer engine versus the coat seller that just said, this is a beautiful coat. And like providing no additional

Grace Shao (39:49)

Right.

Sharon Gai (39:57)

⁓ intricate detail for an answer engine to pull off of. But beyond that, there’s so much more. There’s more on reviews, how well your brand is guarded, general sort of reviews, Google reviews of your brand. And then back in the day,

Grace Shao (40:12)

the credibility.

Sharon Gai (40:14)

Back in the day, these LLMs pulled a lot from Reddit. This month or these months, they’re removing that Reddit portion more and more because people say random things on Reddit. Also, Reddit is 50 % bots anyway. So they’re cutting down the amount of information they’re going to pull from Reddit. They’re now going to pull more from very more credible sources. But to get your...

Grace Shao (40:18)

Mm-hmm.

Yeah.

Sharon Gai (40:38)

a coat to show up, a lot of brands used to do Reddit campaigns. So they would hire an agency and create a subreddit for their brand and make sure that these skews from their new collection was very well reviewed. a lot of them were fake. But that at least it was talked about in the Reddit community because Reddit content was very highly regarded by LLMs. And it’s sort of a moving target.

Grace Shao (40:57)

Mm-hmm.

Yeah. Would you say like,

yeah, would you say then like getting on the AEO is probably like these retailers biggest pain point or choke point right now?

Sharon Gai (41:11)

It’s definitely on the forefront for a lot of them, for sure. ⁓

Grace Shao (41:15)

What other issues are they kind

of facing during this like kind of this AI evolution or revolution?

Sharon Gai (41:21)

⁓

In regards to, well, right now for retail, it’s just a very tough quarter. Like this year, we had no growth in retail in the US ⁓ and the Q4, we might go backwards a bit. So we might go negative one percentage point, but that’s mainly caused by just people want to cut back on spending. People are buying more private labels instead of original, like being loyal to brands. They just want something more.

Sharon Gai (41:48)

bang for the buck, more quality over or same quality for same dollars that they’re spending. And then with all of the inflation, all the tariffs, it also makes goods. There’s just a lot of instability in the market. So everyone is trying to hold on and just maintain and survive. There’s also so many retail chains that’s closed and

Grace Shao (41:49)

Mm-hmm. more expensive. Yeah.

Yeah.

Sharon Gai (42:14)

just gone

bankrupt in the past year. So 2025 has been just a major shakeup for retail. I think a lot of brands want to, their CFOs are telling them to AI-ify their teams and to start employing agents instead of more people and increasing headcount in their teams. But I think for a lot of retailers,

Grace Shao (42:35)

And what kind of agents?

What kind of agents are we talking about?

Sharon Gai (42:37)

Agents for all sorts of every segment of the value chain from product creation, the merchandising piece to buying. So how can you AI-ify your procurement team? Maybe not completely remove the person, but at least stop them from increasing the team and to start outsourcing a lot of the work to AI. To the redesign of websites, writing, copy, creating.

product detail pages, creating marketing content, how do you sell, how do you market this product better to your consumers. All of that can definitely be, your productivity can definitely increase from ⁓ using AI and that’s what a lot of brands are thinking about doing and implementing this year and probably the next.

Grace Shao (43:08)

And that’s where a lot of the work focused on that you do. You help them integrate a lot of these AI tools and the AI agents into their work processes.

Sharon Gai (43:34)

Mm-hmm.

I think a big, actually, missing link from the retail side is there’s no one... When people learn about AI, it’s through today, I read this headline. Tomorrow, I read that headline. The next day, I took an executive education course with MIT and I got a certificate. It’s all... It’s very...

It’s very sporadic and it’s very disparate and everyone has different information points. There’s, I think there’s an absence in learning the foundations and the fundamentals and the applicability, the application part for your business. think those three core things, a lot of retailers are not formally doing because from the tech company side, now you have AI skills as...

Grace Shao (44:20)

Mm-hmm.

Sharon Gai (44:25)

mandatory, a mandatory piece of your job. But retail is so retail is a very traditional business, actually. So they’re there, unless you’re a D to C type of company, so your your team is naturally younger, everybody’s in their 20s, they’re already playing with Sora on their phone every day. You won’t really be part of that sphere.

Grace Shao (44:39)

Mm.

Sharon Gai (44:48)

So what’s needed is a level set of just learning about AI. Yeah.

Grace Shao (44:53)

Yeah.

So if you are, I like this question and I always, I thought it was quirky, but if you were, if you were given a hundred million dollars to really help revamp a traditional retailer, like let’s say Macy’s right. Or Bloomingdale’s or whatnot. how would you actually,

use and spend that money in terms of adopting AI and technology? What areas would you actually spend the money on if you want to make your company future proof in the next five to 10 years with this whole AI revolution?

Sharon Gai (45:22)

I would, instead of looking at where to spend money first, I think I would look back on our business metrics and what number do we want to shoot up more or tweak? Do we want to build our awareness? I think for Macy’s probably not because everyone’s already very aware of this retailer. Do we want our revenues, revenue numbers to go higher? Do we want to increase the number of locations?

Do we want to increase profit? I think I would zone in into an actual business metric. And then from there, cut into what is our bottleneck? So what part of the way that our current team is oriented, are they spending the most time on? Per role, what part of your job should be? Per role, if you were to do a task organization exercise where

There are, an e-commerce manager for instance, in your day-to-day, there’s definitely things that you do repeatedly because every time you upload a new SKU or you’re onboarding a new SKU, there’s definitely repeated things there. And there’s also things that are never repetitive where, know, if for instance, your SKU was just magically picked up by...

Grace Shao (46:27)

Mm-hmm.

Sharon Gai (46:37)

a Sabrina carpenter and now it’s, you know, floating all over the internet. That’s like a one of a kind thing that AI is not really good with in handling and capitalizing on. And so if we figure out the bottleneck and the repetitive pieces of your job to then find the tools that will take away that repetition.

Grace Shao (46:57)

Mm-hmm.

Sharon Gai (47:02)

to free you, to give that time back to you, and to free you in whatever else you can do to make the business better. I think that’s where, that’s sort of the stepwise sequence of things that I would do. And I think that in terms of a dollar figure standpoint, honestly, a lot of these AI companies don’t really know how to best price their products.

Sharon Gai (47:24)

The whole industry is just in a huge moment of experimentation right now, where a lot of people don’t know whether to price it by the more traditional SaaS models, or should we do this in a... A lot of companies are going for a consultancy, like a consulting services play, because Palantir popularized that, ⁓ or like a per time, per query.

Sharon Gai (47:51)

way a usage number and I get a lot of emails from partner vendors from their partner teams and every quarter or so they’re like, we we changed numbers again. This from now on, our pricing is gonna be this for this many queries that come in. So a lot of things are in flux.

But I think the important things that shouldn’t be in flux or that would stay is that those foundational three things.

Grace Shao (48:20)

That’s a very actually thoughtful answer. I was just looking for a headline. But no, I think that that’s really, really fair assessment. There’s just such an early stage in the whole industry right now that these tools don’t even know how to price themselves. But eventually they will figure out their own business models. And then I think for the users, whether these retail companies that you advise or not, they will figure out how to price that into their business.

Okay, mindful of time, I do want to ask, what do think is the most overhyped idea in retail tech right now? This could be AI related or not, but just in general, where do you think people are putting a lot of attention in and whether it’s overhyped or not? What do you think is an interesting retail tech that people might not know about? Actually, that’s a better framing of the question.

Sharon Gai (49:02)

something they might not know about is, well, I have mixed feelings about this, but in 2017, when I, before I joined Alibaba, well, it was right around the year. They actually set up this traveling road show called, Gateway. If, if you’ve heard of that, they did it first in Michigan. So I’ve ever re used to really want to bounce.

Grace Shao (49:19)

No, I’ve never heard of it.

Sharon Gai (49:24)

bond themselves with the Republican Party and go to all these Republican states and say China is a huge consumption power. We’re going to import so much meat and a lot of chicken and a lot of soy and grain. And so they had this traveling conference called Gateway and it would go through different states. And I remember there, there were these Alibaba developers that would make everyone

do this demo of putting on a VR headset and looking at Taobao, like three dimensional. So you can browse through Taobao and through the headset. I think that is something that people might not know about. It’s not so much hyped, I guess. It’s definitely something in the future, but it’s definitely not something here in the next three to five years. I think what we have.

Grace Shao (50:13)

You think like wearables, like wearables for shopping.

Sharon Gai (50:16)

Yeah, wearables for shopping or anything where it’s like a digital 3d store. Like all of the things that we talked about in the metaverse, like those are very, very far away from us. Yeah.

Grace Shao (50:31)

Yeah, I feel like those like VR

sets just like didn’t really take off. They had like a year of hype and then the technology wasn’t good enough. But I do see your point. Like even for myself, like if I could just wear something, I’m sound so lazy, but I can try out outfits and then like, you know, click order. That would be really helpful because right now it’s like I shop on Net-A-Porter and it comes to me that I have to like send it back, right? If it doesn’t fit, but that technology would really change how to shop.

Sharon Gai (50:57)

I yeah. Amazon has that, where you can change your outfit. And then you can also.

Grace Shao (51:02)

Mm-hmm, but it’s not feel like

as real yet, right? Like it doesn’t fit my body, my changing body and you know, like just like, it’s just like a fixed avatar, right?

Sharon Gai (51:13)

Yeah, it’s two dimensional, you, but you, it’s hard to do that too with a VR headset.

Grace Shao (51:15)

Mm-hmm.

I see what you Okay.

Sharon Gai (51:20)

Yeah, you would need some sort of mirror to do like that sort of, that was, that was a tech that was big too and then also died, which is like in-store interactive displays where guests implemented it, where you don’t have time to try on all those outfits. So they uploaded every single.

Grace Shao (51:36)

Yeah.

Sharon Gai (51:43)

item into this mirror and you can just plop plop the mirror and you’re standing in front of the mirror so you can see, so you can check out your outfit without actually physically putting it on. ⁓

Grace Shao (51:55)

Yeah, Lululemon had the fitness

ones, right? The fitness mirrors. then they kind of, it was the same technology behind it, but it also kind of died after COVID. Although I think a few Chinese companies are trying to make it happen again. They’re saying it’s AI empowered. But right now it doesn’t feel like much more than a big screen where you get to see reflection kind of just following the structures and instructions and that’s it, right? It’s not huge breakthrough yet. Yeah. I have one last question for you. Cause we were kind of going off on our

Grace Shao (52:22)

tangent again, the two of us, they first started with cows and now it’s like the mirrors. What is one unique belief or differentiated view you may hold about industry that you think is quite different from others in the industry?

Sharon Gai (52:35)

on the note of retail, I think.

Grace Shao (52:37)

on the note of retail and or retail and technology.

Sharon Gai (52:40)

I think retail is pretty simple. I think people complicate it too much with all these bells and whistles. At the end of the day, retail is how do you sell something and make the other person interested? That’s all. How do you take any product that you have, you can put any sort of shell over it, and someone’s eyes light up.

That’s what retail is. I think all the AI that we add, the VR things, the personalization and this or the other, those are all ways to do it. But I think it’ll just change. So I think...

I think not enough retailers go to the heart of what they’re selling or what they’re doing. And they often, it’s also understandable because, know, it’s most like in any Pyramidic industry, you always look at what the top key account or top brand is doing and they’re usually producing the most GNV. So you say, ⁓ they’re selling a lot. I’m going to do what they do.

And then everyone kind of swarms in that direction. And it’s not that, I think that’s for any industry, that’s just how I guess the world works. But I think the right way to approach it isn’t to look at that model. I think it’s more just to answer the question of what are you selling and how are you gonna sell in a way that attracts attention.

Grace Shao (54:06)

I think that’s like pretty applicable to like every industry. Like when you’re saying that, like really I was just thinking about like the business of media content or anything, right? Like fundamentally go back to like what, what is the problem you’re trying to solve? What answer are you trying to provide? What are you solving? Right? Like, and then, you know, go find your core versus all the kind of frivolous things outside of it.

Those are just tools. anyway, really, really appreciate your time. And I actually really love that you just walked us through the evolution of technology and retail, the intersection of technology and retail from, you know, what we know of e-commerce marketplace to live streaming to, you know, AI adoption. And we kind of even touched on our VR.

AI Proem is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Joining me today is Chim Lee, Senior Analyst at the Economist Intelligence Unit. He works in EIU’s China and Asia teams, and is based in the company’s Beijing and Hong Kong offices.

He leads EIU’s research on China’s advanced technologies, Climate change, Energy, Semiconductors, and Artificial intelligence, and also covers how China’s industrial policies link up with the broader diplomatic and macroeconomic dynamics.

Our conversation starts with China’s newly announced 15th Five-Year Plan proposal, which reveals the country's next priority and how it may impact its economy, society, and trade relations with the rest of the world.

We then dove into the current involution 内卷 issue, particularly zooming in on the solar and EV sectors. Then we look at the data center build-out driven by the AI boom and how local and regional governments are making sure involution does not hamper this sector.

Finally, Chim reflects on his work and his analysis of China’s economic planning and innovation direction.

--

In today’s world, there’s no shortage of information. Knowledge is abundant, perspectives are everywhere. But true insight doesn’t come from access alone—it comes from differentiated understanding. It’s the ability to piece together scattered signals, cut through the noise and clutter, and form a clear, original perspective on a situation, a trend, a business, or a person. That’s what makes understanding powerful.

Every episode, I bring in a guest with a unique point of view on a critical matter, phenomenon, or business trend—someone who can help us see things differently.

For more information on the podcast series, see here.

They delve into the strategic priorities outlined in the plan, including self-reliance in technology, maintaining manufacturing dominance, and the role of private and public sectors in driving economic growth. The conversation also touches on the challenges of overcapacity and the evolving landscape of China’s international cooperation.

--

* China’s Five-Year Plan signals strategic priorities.

* Focus on self-reliance in technology and innovation.

* Maintaining manufacturing dominance is crucial.

* Private sector plays a key role in economic growth.

* Overcapacity remains a challenge in various sectors.

* International cooperation is evolving in China’s strategy.

* AI and new energy are critical emerging industries.

* China’s economic planning involves both public and private sectors.

* The plan addresses geopolitical tensions and trade flows.

* China’s approach to technology is both strategic and adaptive.

In this conversation, I spoke with Tom Nunlist from policy consultancy Trivium, about China’s AI Plus plan and its implications for the economy and society. We discussed the role of digital infrastructure in AI adoption, the transformation of production relations, demographic challenges, and the government’s role in connecting academia and industry.

The conversation also covers the complexities of navigating China’s regulatory landscape, municipal and provincial implementations of AI policies, and the measurement of AI’s economic impact.

Tom shares insights on how MNCs can better align corporate strategies with government objectives during the AI growth era, and talks about the emerging AI pilot zones and how China balances between innovation and regulation.

Tom Nunlist is the Associate Director of Tech and Data Policy at Trivium, a leading China policy research consultancy. Tom’s work explores the intersection of politics and technology, with a focus on data and artificial intelligence. His hands-on consulting work with Fortune 100 clients covers policy analysis, risk assessment, government relations, and communications.

In today’s world, there’s no shortage of information. Knowledge is abundant, perspectives are everywhere. But true insight doesn’t come from access alone—it comes from differentiated understanding. It’s the ability to piece together scattered signals, cut through the noise and clutter, and form a clear, original perspective on a situation, a trend, a business, or a person. That’s what makes understanding powerful.

Every episode, I bring in a guest with a unique point of view on a critical matter, phenomenon, or business trend—someone who can help us see things differently.

For more information on the podcast series, see here.

Chapters

04:27 Understanding China’s AI Plus Plan

10:55 Transforming Production and Society with AI

15:54 Government’s Role in AI Development

24:59 Measuring AI’s Economic Impact

27:12 Local Adaptation in Policy Implementation

28:01 Understanding Chinese Policymaking for MNCs

28:59 Aligning Corporate Goals with Government Objectives

31:19 AI Pilot Zones and Innovation Hubs

33:26 Promising Use Cases for AI Adoption

35:56 Balancing Innovation and Regulation in AI

42:52 Shifts in Government Priorities for Technology

45:56 Tracking Real AI Diffusion in the Economy

48:57 The Skills Gap Created by AI

AI Generated Transcript

Grace Shao (00:00)

Joining me today is Tom Nunlist, Associate Director of Tech and Data Policy at Trivium, a leading China policy research consultancy. Tom’s work explores intersection of politics and technology with a focus on data and artificial intelligence. His hands-on consulting work with Fortune 100, clients, covers, policy analysis, risk assessment, government relations, and communications. Tom, it’s so great to have you here and it was lovely meeting you online actually a couple weeks ago at one of the panels we were on together.

So today, will unpack China’s AI Plus plan, what it means for the real economy and how AI governance is viewed by China, sorry, viewed in China and compare that to what’s happening really in the US. But to start, tell us about Trivium and what’s your own professional journey. How did you kind of end up in Shanghai?

Tom Nunlist (00:45)

Cool, thanks, Grace. That was a nice introduction and likewise, good to meet you and good to be here on your podcast. Definitely flattering. I’ve seen your upcoming guest list and lots of exciting personalities coming up to be on the podcast. So yeah, a little bit about Trivium. We were founded ⁓ in, I think, 2017. So we’ve been around about eight years now. We are a China-focused, or right now China-only policy consultancy.

⁓ And so we really our kind of like value ⁓ is that we really know how the sausage is made here in terms of policy and politics in China and we help our clients mostly multinationals and investor clients understand that. So, you know, for example, a new policy like AI plus, you know, comes out, you know, we can come in very quickly and, you know, help inform our clients, you know, what this is, where it comes from, its overall context.

and then forming scenarios for how it’s gonna play out and kind of what they might wanna do. As you mentioned, I’m on our tech team, but we cover a lot more ⁓ than tech, really kind of the whole nine yards of ⁓ policy making, be it from economics to labor to kind of everything in between.

Tom Nunlist (02:01)

Yeah, as for for myself, I think I have a pretty typical China story, you know, insofar as, you know, long time expats. I came here in 2008, you know, more or less on a lark as a study abroad students, you know, to figure it all out and, know,

then life happened, got interested in it. I moved here permanently in 2013. My undergrad background is in journalism. So I studied here for a bit. Then I worked at a business review magazine and then eventually kind of made my way into the consulting space. Not too much of a very strict career plan, but again, one thing sort of leading to the next and here we are.

Grace Shao (02:43)

Awesome. So I think we’re going to go straight into it. What everyone’s interested right now is in the AI Plus plan. So that rolled out in August this year, believe, late August. It’s quite new. think people are still trying to understand what it really means. So the Chinese State Council published a high level paper that was basically pushing all sectors to really embrace AI.

It’s said to be the most comprehensive blueprint for AI development domestically, and even touches on China’s international ambitions or diplomacy as well. So to start off with, can you just tell us, high level, what is this really about? How do we understand this policy?

Tom Nunlist (03:24)

So this is the second high level AI policy to come out of China. The first was some years ago already back in 2017. That was about, you know, it was about the new generation of AI. AI Plus, the concept has been around for two years already now. It was originally announced at the two sessions. Hopefully our listeners know what that is. an annual meeting like that sets policy. It was announced two years ago, talked about again this year, but you know, not many

details were revealed about it. There was some assumptions which they were correct that it would be a bit like a former policy from 2015 called Internet Plus, was kind of following in that same vein. And just to sort of like set the stage of like what a document like this is, so it’s called a State Council opinion, you know we’re referring to it as a plan, it’s called Plan in the name, but it’s really a sort of like high level

like political document that is setting the priorities for what the nation wants to do, right? Like here’s the direction we’re gonna move in. Here are some like broad, you know, over the horizon kind of KPIs. This is where we all wanna go. ⁓ And then, you know, the plan will cascade kind of down from there. We’ll get more details over time. In terms of the name, AI Plus, that’s AI Plus or added to everything.

So everything in the economy, in all sectors, in society, we wanna see, the government wants to see AI make its way into there to get the most use out of it, to really ultimately transform the way that the economy and society works. It’s big, it’s a big vision is the answer.

Grace Shao (05:04)

think it’s really interesting you mentioned Internet Plus because I remember when that came out. So you said roughly 10 years ago, It was really embraced by every part of the economy and society. So have you seen any attitude changes or shifts or how people view AI Plus just being on the ground in China right now?

Tom Nunlist (05:22)

Well, in terms of views on the ground, like in terms of people talking about it publicly, not a whole bunch. I think what’s really happened here is these both internet plus and AI plus are responding to things that are already happening. So the internet certainly did not arrive in China in 2015. was ⁓ like.

Grace Shao (05:42)

Yeah.

Tom Nunlist (05:43)

very, very much going strong and was already one of the world’s leading digital economies at that time. And so what it was really seeking to do is kind of take the momentum for things that were already happening and push that further. So obviously, in 2015,

you know, the consumer internet, know, Alibaba, Taobao, like those type of, certainly WeChat Pay was introduced in 2013. You know, these were making waves, making big changes in the way that society just kind of basically works. And then Internet Plus was like, yeah, let’s take this momentum and apply it to everything else. Let’s have Internet Plus healthcare. How can we use the Internet there? You how can we use it in government services? And again, AI Plus is sort of doing the same thing. You know, the, you know, this is an introducing the AI wave to China, the AI wave is here, it’s happening, everyone’s using it, everyone’s excited. And so this is getting behind that momentum that is naturally already here and attempt to build a policy framework around it and like, yeah, really, where are we going with all this momentum, right? What are we aiming to achieve?

Grace Shao (06:52)

Yeah, I think actually on that, I am curious, has China’s highly digitalized society and the infrastructure made AI implementation or diffusion any easier in your eyes? Or how has that kind digital infrastructure played a part in just the mass consumer adoption of AI we’re seeing right now in China?

Tom Nunlist (07:05)

Mm-hmm.

Yeah, it’s a huge part. mean, from just the consumer side, China is like the US or Europe, just an extremely connected society. Everyone, even in the most remote places in the country, has their smartphone, has probably even has like 5G.

WeChat is something of a national infrastructure at this point. It’s a messaging app that everyone uses for work and life. It really is absolutely indispensable. And so having that infrastructure already there, or having everybody with a phone in their pocket automatically makes these tools accessible. I think...

any age, any person you might come across, you know, do they have DeepSeek on their phone? Chances are, yes, they probably do. On the back end, which I think is, you know, just as ⁓ important. So the past few years, or as we all know now, right, the...

know, the biggest part of the biggest spend of the AI boom is building out, you know, massive, massive data centers, right? And making that kind of infrastructure work. It’s a huge race right now in the United States. And so there was already a national plan to have ⁓ a nationwide network of data centers, you know, put in place as kind of before this big AI wave.

It’s a bit to do with some broader reasons of internet and energy and having some of this infrastructure in place. ⁓ Actually, in terms of energy, that’s one of the ways that I think a big leg up that China has in terms of the US is the amount of energy infrastructure it has built out compared to other parts of the world. So they’re ready to sort of do this, where I think other places maybe a little bit less so.

Grace Shao (08:57)

Yeah, and I think we can kind of talk a little bit more about the East data, West compute and all these different government initiatives that’s really boosted the data center built out. like, to your point, priority even the AI boom. But actually, let’s take a step back and kind of look at how the policies really affect a society, right? I think in September, one of your colleagues Kendra, her shape for a road, a blog post saying,

The state council’s AI Plus directive is to reshape the paradigm of human production and life. When I read that, was like, what does this mean? It seems kind of crazy. Like, are we going to make AI babies now? What does it mean to promote a revolutionary leap in productivity and profound changes in production relations and accelerate the formation of a new intelligent economy? So how do we kind of like break this down? What does it mean? And, you know, when we were chatting prior to this podcast recording, you said this is a grandiose term.

It’s three-shaping human production, but what does that actually mean? Like, is this quite literally reproduction? Like, how do we understand that?

Tom Nunlist (09:54)

Well, I don’t know, hopefully reproduction will stay traditional. in terms of, know, these types of policies sometimes have these like really big grandiose framing. know, again, back to what I said earlier, the point of this, it’s a political document at the end of the day. It’s establishing a vision, right?

and the promise of AI, which is not... ⁓

news to a Western audience is that it will be transformative of society that will kind of like change sort of how things work. This specific language that they’re using there, like talking about transforming production, you know, that’s a bit in the sort of like communist Marxist language, you know, of China. And then the context that it’s kind of living in now as well, ⁓ there’s this like really big deep sense of urgency in China of like kind of like

the need to move from ⁓ an economic model that is waning, that sort of reliance on labor intensive ⁓ economy and land sales and things like that into a ⁓ new area where they’re get new types of growth, new and better growth, the switch from quantity or quantity.

⁓ to quality. These goals were kind of already there. There’s another, you know, wonky Chinese policy term called new quality factors, new quality production factors, and what are all of these, you know, types of new things, you know, ⁓ AI, self-driving cars, and so on. And...

it wants to leverage these into making new growth opportunities happen, basically.

Grace Shao (11:33)

Yeah, and I think you touched on one thing, which is like, you know, the traditional economy is very like labor heavy and it really relied on just the mass population of the mass workforce, right? But what we’re seeing right now in China, but not only China, a lot of like actually very developed economies across Asia, including Japan and South Korea right now.

is that there are just not enough people. Like the population is declining, people are not willing to have children, right? And kind of given that backdrop of an aging population, a shrinking population, what is kind of, I guess, the goal from the government when we look at the labor force? And how will AI and technology play a role in that? Are we really just going to see like robots implemented or is it more automation? Or how do we understand this? Yeah.

Tom Nunlist (12:22)

think in some cases, we will see robots replacing physical workers. ⁓ But I think that’s the smaller part of the story. The bigger part of the story is this broader question of actually just avoiding the middle income trap. And so in order for China to take care of its aging population, to sort of weather this big demographic shift that is happening now,

no matter what, even if birth rates double or triple next year, it’s gonna happen. And the way to do that, that the government sees is to raise people’s incomes per capita.

and do that very quickly over the next 10 years, More profitable companies have more prosperous people, have a bigger tax base. And so that the country is just able to deal with this challenge as it emerges. Again, it’s inevitable. And so back to this new quality production factors or this transformative effect that AI is gonna have, the transformative effect is that it will be a productivity multiplier, right?

enabled everyone and companies from big to small to be vastly more productive and vastly more valuable and really help China earn enough and become wealthy enough, maybe right before it ages too much. So I think a bit indirect there, but it’s about the whole economic story together.

Grace Shao (13:51)

Yeah, and I think the whole approach to AI development and progress has been extremely pragmatic and economic driven for China, which is a bit different from what I get the sense in DC and even for sure Silicon Valley. Actually, on the topic of what you just mentioned, which is the government’s role in promoting companies and companies’ profitability, I have heard of this thing where the government is playing a role becoming a networker between the academics.

Tom Nunlist (14:00)

Yeah.

Grace Shao (14:19)

and the researchers and the companies. And I think for the audience, a lot of people sitting in West, we know about Alibaba Tense and Huawei, these mega big tech companies having talent schemes, quite similar to how basically there’s campus recruitment for like Meta and Google, whatnot, right? But how is the government now playing a role for SMEs or even smaller companies in terms of how are they connecting talent and...⁓ policy people and kind of the resources in the public space and the private space.

Tom Nunlist (14:51)

This is a great question, think, and a really important thing that’s emerged just over the last couple years. It’s not just AI, it’s sort of like all areas of science, technology, and engineering. But what it seeks to do is to bridge the corporate world and the academic and research world in a better way, right? So you can have like...

⁓ needs and talents and coms flowing both ways. So for example, this might be setting up round tables or some kind of like platform or any kind of mechanism that brings these parties together. So going in one direction from corporates, setting up links with universities so they can go into departments and say, hey, we’re doing biosciences, we’re doing AI, we’re doing you know, some type of metallurgy, you know, these are the types of talents we need. And can you focus on that? Can you help get us, you know, train that talent that we need?

or going the other way, having researchers see what’s going on in the corporate world and having a solution for that, or green fielding their own research, right? They’ve been doing this for a state institution or a university, and now they wanna take it out of there and find the right entrepreneurial partners to do that with.

Right. You know, as you mentioned, know, like large companies have done this sort of thing for for a very long time and have prospered, you know, because of those links. mean, indeed, I mean, a lot of the American tech giants, you know, came. That’s a famous story. It came out of a university or dropped out of a university, you know, and now, you know, maintain those links. It’s same in China, but, know, that’s a lot harder to do if you’re an SME. I mean, everything’s harder to do if you’re an SME because you don’t have the resources. Right. So providing that meeting place,

facilitating that is I think a really important program and one that I’m pretty confident will see solid results in the next couple of years.

Grace Shao (16:49)

So what agency or what government entity is actually helping facilitate that kind of meeting right now? And this question is to lead to the next question, which I’m going to actually ask now, which is, for me, I’m not a policy person. I’m getting confused when I read these papers, right? Because there’s the NDRC, which is in charge of the economic planning.

Then MIIT, which is in charge of the information technology, the ministry, the CAC, which is a cybersecurity regulator, right? Then there’s the MOST, and then there’s a party central science technology commission. There’s just so many of these government agencies that seems to be all involved in pushing the progress of AI technology at this point, as well as being a regulator for safety and policy work, right?

Could you kind of just break that down? Who’s in charge of what?

Tom Nunlist (17:39)

All right, okay, let’s just address that one, because that’s like a pretty big question. So for this, for the AI Plus plan in particular, the main administrative body for this is the NDRC. So for those of you don’t know, that is the macroeconomic planner. They’re in charge of kind of like setting the big direction.

of the ⁓ economy, right? So NDRC is in the coordinating role of this plan, right? So from there, it’ll go to the other ministries of the state council, some of whom you’ve just mentioned, right? So the industrial ministry, that’s MIT, science and technology ministry, most. ⁓

⁓ CAC, the cyberspace administration, all across the board. And those ministries will be in charge of taking the big idea and making it specialized or setting specific goals for their various sectors. And we’ve already seen that happen, actually. just a couple of weeks ago, the National Energy Administration, the NEA, came out with the very first ministerial AI Plus plan, which is AI Plus Energy.

And we’ll skip most of the details there, but suffice it to say, it is gonna use AI to help make the energy transition happen, which is very cool. From there, it will cascade down further into localities. And localities is really, that’s where the rubber meets the road and where all of the action happens. So we’ll see cities, they’re already AI plus plans.

There’s one in Beijing and Suzhou, those are explicit. And then like Shanghai has one basically, although it’s not called AI Plus, but they have one as well. Interestingly enough, those also actually predate the national plan, which is something that kind of happens in China at various points. And so a lot of the like actual like funding decisions and a lot of where the funding comes from will be at the local level.

And then there’ll be like, you know, a national pool of money as well that will like help support those, right? So, you know, it’s a top, you know, people say, China’s a top-down system. That’s of course true. And what I just described is how that top-down system works, right? So from the central planner down to ministry needs, down to local level, which has all of those ministries at the local level and, kind of being funded ⁓ from there. And then of course there’s like special national projects here and there.

Grace Shao (19:59)

So I’m just trying to understand this. In the RSC, the economic planner basically makes a big grand plan and they push out the AI Plus that we’ve been talking about that was pushed out in August. But a lot of the execution that’s done is actually trickled down into localities like the local governments, the provincial governments, city governments, whatnot. And so something like, just taking this as an example, something like the facilitation of maybe a researcher at Tsinghua meeting private company for potential, let’s say commercialization plan, that could be actually led by say the Beijing Education Department or how does that, I just wanna understand how to execute that works. Okay.

Tom Nunlist (20:36)

Yeah, yes, yes, yes.

Those might exist at different levels, I’m not sure. But yeah, the local level would certainly be implementing stuff like that. Or in another more sort of ⁓ direct way, Shanghai has money now where it can like say, companies that are in AI space are eligible for X amount of money.

funding for their first year, right? And like that funding decision, that’s made at the local level at Shanghai.

Grace Shao (21:03)

And that will be decided, I guess, by what the city might mean. So each city, each province, given their strong, they have their own economic factors, right? Like for example, like I think I was researching Harbin, like, you know, people think it’s just like a really cold place for the ice festival, but actually it’s an industrial city with a lot of legacy in robotics, traditional robotics, mechanics, industrial machinery. So their money might be put into developing physical AI.

Tom Nunlist (21:12)

Yes.

Grace Shao (21:29)

like embodied AI, right? And then maybe in Shanghai, we’re thinking about like maybe consumer driven products, right? Like just, just kind of high level thinking, but that that that’s kind of what happens. ⁓ So I want to understand how does the KPI work then, like in terms of like, how do we understand, I guess, how these, because what I’ve heard also is like these cities to cities, compete with each other, they compete for talent, they compete for, like, bringing in different businesses, how does that work? And then in terms of like, how do they actually

Grace Shao (21:59)

a measure, right? Like the technology or AI’s contribution. Because we talk a lot about, like people talk a lot about like how companies are trying to measure AI’s like actually, ⁓ you know, contributions to the company right now, the profitability. How do we actually understand AI’s contributions to the economy? I guess it’s two separate questions, but yeah, help me understand that.

Tom Nunlist (22:19)

Yeah, this is a really interesting question. And I think frankly, it’s one that the government is just trying to figure out itself. For years, of course, it was just GDP. So you win if you bring your area GDP, which is great for encouraging growth until it encourages the wrong kind of growth or encourages the wrong kinds of projects. And so I’m a little bit less familiar off the top of my head, but it’s something my colleagues have looked into. ⁓

as well is how these KPIs might be changing. And again, from this shift from quantity to quality, I think at the end of the day, probably something like GDP is simply the easiest thing to sort of see. But certainly, and that’s like if you’re like a mayor, I guess. But for people that maybe work within different ministries or in like...

specialist areas, whether or not they do a cool project along these lines, whether or not they brought, they fostered the emergence of a new giant in their district. That’ll be looked on favorably. So in terms of who actually sets these KPIs, I think that would actually go down to the personnel department ⁓ and how they interact, how the personnel department decides

things to include on there, some of which will be from NDRC’s AI plan and some of which will be from like totally other different things. I can’t tell you what their score rubric looks like. But again, the message here of going this like broad top-down kind of thing, what officials will be doing is, you

looking at the communication of these targets, right? Looking at the messaging and interpreting them for their district, right? So what do I need to do to make that happen here? And that’s the way forward for my career, right? And also to connect this with what you were just talking about in terms of local specialization, right?

what’s going on in Harbin, the local conditions there are different from in Shanghai or in Hangzhou. And so I think in the ideal way, and the government uses this phrasing a lot, is to have things definitely specially adapted for your local conditions, right? Don’t just do exactly what we’re saying, like make it work for you.

Right. And so in the ideal world, you would have like different things going on everywhere and they would all be complimentary. I think what happens, what tends to happen is that you have duplicative efforts, you know, which of course we see, you know, everyone’s talking about now in the auto industry. my gosh, there’s a hundred auto companies and they’re all, you know, in a giant battle Royale that is destroying value, you know, rather than. Yeah.

Grace Shao (25:06)

Yeah, the price war right now. Yeah.

Actually, how do we understand this? think because for the sake of, know, understanding Chinese policymaking for say, Western investors or Western companies, like say, MNCs operating in China, and in the day is to help them

better their operations, right? So then how do we understand this from that perspective? Say your client’s M &C and they’re saying, seeing, okay, AI plus is being rolled out on a central level. Then they are like, how do they decide? I don’t know where to put their plan, to build out their operations. How do they kind of make that judgment comparing provinces to provinces? And I think to your point, you kind of have...

answer this in the sense of like maybe if you’re industrial machinery you go to Harbin right but if you’re consumer goods you’re Shanghai but are there any other things that companies need to be aware of or investors investing in companies are coming out of these different problems should be aware of?

Tom Nunlist (26:00)

Yeah, great question. So I think probably the first choice, the first thing to look at is just, you know, where are the hubs for what you’re doing, right? If you’re an automotive company and you’re looking to make any of these, well, might go to, you might go to Enhui, right? Hefei, sorry, I forgot it for second. You might go to Hefei because that’s where a lot of the new energy vehicles are.

Right, and then from there, and this I think is a bit more unique ⁓ to China, is if you’re a corporate and you’re trying to be successful here, one of the first things you need to do is align with whatever the government is trying to do. You know, that doesn’t mean do exactly what the government asks you, right? But you know, figure out what officials there want, what their KPIs are, what their existing programs are, and how do you align your corporate goals with that?

⁓ And that’s how you get support. That’s how you get buy-in. That’s how you’re ultimately successful, right? You know, as in sure it’s no secret to anyone, you know, the Chinese government just has a much bigger voice in the direction that the economy is going, right? And the things that are happening in the economy and, you know, companies and investors absolutely, you know, have to listen to what that voice is saying.

I think for investors as well. So where are these companies collected? Where are the big hubs for the industry that we’re investing in? And also, what is the government itself saying that it wants? And which companies do we think can...

Obviously, of course, first deliver on the market promise, like do what they’re saying you’re trying to do, but are there opportunities here? Will they get this kind of support from the government that is a factor that is larger here than it is in other places? Probably maybe any other place.

Grace Shao (27:46)

Yeah, I think it’s also like the point you’re saying, it’s not really like you have to do what the government says, but it’s like you might as well lean into, like, I guess, lean into it, right? Like there are going to be favorable policies for your industry, certain areas, municipal areas, you might as well lean into it to optimize or to like maximize your success rate or your success possibility, right? So on that

Grace Shao (28:10)

point actually, I’ve heard that there are quite a few AI pilot zones. Like, you know, right now, I think for the West, people only know about Shenzhen, Hangzhou being kind of the tech innovation centers, obviously Beijing, Shanghai playing a big role for corporate headquarters and obviously where investors sit, policymakers sit. What are some other major cities that are actually quite relevant to this like AI growth right now or are considered AI pilot zones?

Tom Nunlist (28:35)

think those would honestly be the main ones. know, Shanghai, Beijing, you said, Shenzhen, Guangzhou, Hangzhou, like these are the places where, you know, a lot like the most action is happening, right? Especially in an area where we’re talking about, I mean, it depends on what we mean, right? So like if we’re talking about just raw AI development, making new LLMs and stuff like that, you know, one of the big, you know, stories is that there’s only so much talent out there that can do that.

⁓ and this talent will gravitate towards some center. And there’s only a few of those, only, not everyone can have those people. Not everyone, those people won’t go everywhere.

Right, AI, but back to what AI Plus is about, right? AI Plus, all of these other things, right? And having that in various sectors, I think where other cities will excel or have the opportunity to excel is where those hubs are, right? So if we’re trying to add AI into auto manufacturing, that’s gonna happen in an auto manufacturing hub.

Right. And I think that actually speaks to the important thing that folks need to be looking out for. You know, at this point, know, we’re, you of course, at the high level, you know, we’re talking about sectors. OK, we AI in the research sector or want AI in the health care sector. But I think what’s most important is going to be looking out for not which sectors it revolutionizes, but which specific use cases, right, are going to be.

Grace Shao (29:59)

Mm-mm, I see.

Tom Nunlist (30:06)

most obvious to implement.

Grace Shao (30:08)

And actually on that point, which use cases, let’s put it that way instead of sectors, do you think are kind of showing the most promising mass consumer adoption of AI, gen AI as we know it? So I’m not talking about like the buildup of LLMs and everything. I’m saying, know, when DPC came out, there was a media frenzy of stories about how China’s like home appliances are even adopting AI, EVs are trying to adopt AI, you know.

Tom Nunlist (30:13)

Yeah.

Yeah.

Grace Shao (30:34)

I mean, obviously that kind of hype has gone, like, moved past us, but like, in terms of whether you want to use sectors or use cases, where do you see actually China right now really leading in adoption? And where do you think we’re seeing the trend going towards maybe in the next three to five years?

Tom Nunlist (30:50)

Yeah, think ⁓ it will continue to penetrate more ⁓ on the consumer side, just on of like AI services that are available to everyone. mean, that’s sort of the biggest thing right now. Whether or not we can get consumers to pay in China, I think is a little bit different of a question. But in terms of specific areas, I think it’ll be where we’ve already seen AI ⁓ have quite a bit of traction. So in like logistics and transportation where, you know,

with like self-driving is kind of almost here and you know we have the the nev is this it’s a software-defined vehicle and we’re going to be like a ready integration for ai into the features of the vehicle that’ll definitely be one you know another one thinking about ⁓ that comes to mind is is agriculture which i you know ⁓ i can’t name a specific company or or a project but ⁓

you know, drones are becoming ⁓ large and, you know, helping to manage big farms, like do things like crop spraying, you know, or inspecting or like, also not just in agriculture, in inspecting power lines, drones are not used to do that. It’s actually physically hard to get up there, right? And so there’s AI use cases for that, right? It can go into like visually inspecting, right? Or visually help, you know, irrigate your crops and so on and so forth.

So it’ll be things like that, right? Where we’ve already started to see new things happen, AI being used a bit. And now these new tools and the growing power of these tools will enable it to really actually happen.

Grace Shao (32:28)

Yeah, definitely. think like, when I first saw and tried out a few of the EV cars, this is even like during COVID, this is like three, four years ago, I was shocked by I wouldn’t say they’re like genuine power, but how tech savvy they already are. had voice control, each of them already had a built in robot, you can control your like windows, you control your heat, like the heat of your seats by voice recognition, voice control. And I think like you said,

Tom Nunlist (32:42)

yeah.

Grace Shao (32:52)

implementing GEN.AI into it just means that it can actually embolden it more, right? Do more things or right. So that’s really interesting. I think I want to double click on one question that a lot of people are kind of debating. know, China’s approach innovation often is said to be, you know, innovate and then regulation comes later. Europe obviously takes another extreme case of like hyper or not hyper, but like a lot more.

Tom Nunlist (32:57)

Yes.

Grace Shao (33:16)

cautious and safety, you know, safety cautious and like, you know, regulation comes first. And some people are complaining about how it’s hindering innovation or innovation going into production. Right. So I guess my question right now is you’ve been in the AI safety and policy space for a long time now in China. Do you think that actually you must give up safety for innovation or are there other ways that you’re seeing people actually being able to have safety and

innovation co-exist and co-develop and maybe taking data privacy as an example or how did Deepsea come through if there was so or let’s just talk about that space.

Tom Nunlist (33:50)

you

Yeah, this is an excellent question. And frankly, I think one of the most underappreciated or even like misunderstood aspects of the AI story as it stands right now in China. I mean, there was a point not too long ago before the EU AI Act, which you mentioned where, you know, China had, you know, the strictest AI regulations on the books in the world. And yet, you know, DeepSeq was still clearly able to emerge here and,

you know, become what it is, right? And I think the story here is that, you know, China is, I think, as most people will understand, a very security conscious, you know, country, but it is also highly flexible, right? And the interesting thing, the sort of interesting story, like when ChatTPT first came out, there was this mad scramble.

among regulators to get a handle on it, right? Because it was gonna flood the internet, you know, with these tools and man, what are the impacts gonna be, you know, like just a real sense of urgency to try to like write something immediately. And so there was a period of about a year and a half where you had regulation after regulation and, you know, they...

you know, if you looked at them in line, you’d think they were different, but they were actually kind of rewriting one another, and it was like all very ⁓ messy and a very confusing space. But then, you know, China was able to kind of like find what its bottom line is.

and then be flexible and adapt from there, right? So it was, you know, hurry up, let’s do something. Let’s kind of see what’s gonna work, where it might be too far, and then dynamically kind of like dial back.

So one of the interesting, I think probably the most interesting single event of this story was there was a registration system that was created where if you want to publicly release an AI tool like a chat TPT, it has to be registered with the state and blah, blah. And then some requirements started to be built on top of that. And there was a draft that said at one point,

all of your data that you need to train your LLM with needs to be verified as true. And the AI research community came back and said that this is impossible. Like if this is implemented, will, know, progress will grind to a halt. There is no way we can do this, right? There was no official response to that, but the final version of the rule did not contain that.

Right, was that was walked back. was an idea that was tried out, that was an explored, you know, and eventually, you know, was abandoned because it didn’t work. And so I think, you know, one of the sort of like, again, underappreciated or even unknown strengths of China’s regulatory system is that it can be flexible in that way.

Grace Shao (36:27)

Right.

Tom Nunlist (36:46)

in an ungenerous interpretation of this, which you hear from a lot of foreign companies and rightly so because there are drawbacks of this, is that regulation can kind of seem all over the place and arbitrary and you never know what things are gonna change next.

And certainly in emerging areas, that is true and very challenging, you know, if you’re in a corporate compliance type situation. But the plus of that is it can be, you know, quite flexible and adapt to, you know, what the perceptions of the needs are kind of as they’re coming up, which in, you know, an environment as fast developing as this one, where again, new problems might emerge tomorrow. I think that’s a really important strength or really useful strength to have.

Grace Shao (37:29)

We could be quite reactive in the sense that they would actually react to what the industry and the actual practitioners at the leading frontier, technology development, want or need, right? To really help and regulate the technology, yet also not hinder any progress. I think that’s really interesting and it’s a very fresh take on it. I haven’t really heard that before, but I think it’s

it makes sense. And it also kind of explains what you said about some people’s kind of complained or misunderstanding of this whole like murkiness. so you said that the AI Plus initiative really it’s been around, like not been around, but like the AI policy or the plan has been around or the idea has been around. And then there was a 2017 National General AI Plan as well,

There’s also the made in China 2025 plan, all these big grandiose plans that have been really pushing forward AI or robotics and just technology development in general. as you said, policymakers and regulators can actually be quite reactive. So over the last, I guess, 10 years as these three mega plans been rolled out.

How have you seen these things change or how has the policy makers really a change in terms of their sentiment or the attitude towards this technology?

Tom Nunlist (38:40)

Let’s say the biggest thing, so taking a bit of a longer view, so science, tech, and manufacturing development has been a priority of the governments for a very long time, since the late 90s. It’s been kind of on this top priority list. And so one shift I’ve seen in the past few years is side tech development moving from one of the list of important things into the top thing.

like the most important thing. It’s like that is ⁓ kind of an organizational principle, right? Or like a driving organizer of the whole party, right? And again, that’s because of the perception of what the state’s needs are at this point. In the past, in the sort of like last formulation, right? Of like what the country needed, right? It just needed growth.

It’s like, it’s the late 70s, we’re into the 90s and 2000s. We need to just grow. We need more people and jobs, we need production. That’s what we need. Now that’s not what they need. We need quality growth, we need to move up the value chain, we need to avoid the middle income trap if we can, expand people’s incomes, become a more efficient and a more technologically driven society. And so the sort of prioritization,

and some of the character of these plans have changed sort of in line with that. Some other things I think have stayed the same or strengthened rather, right? So with Made in China 2025, which this not really talked about explicitly anymore because of the political sensitivities it creates in the US, right? But the sort of view, right, was that China

you know, doesn’t want to be vulnerable, basically to, you know, always reliant on outside technology and wants, you know, these things for its own, right? Wants them to be secure and controllable. It wants to have, have its own thing, right? That of course, I think, you know,

in light of the subsequent ⁓ US effort to strangle the semiconductor sector in China is even more of a priority. So it’s not just move the value chain and get incomes up, it’s also create these fundamental technologies which we absolutely cannot have as a vulnerability.

Grace Shao (41:03)

Essentially kind of push more honed in on the self-reliant focus than they previously didn’t really have to, right? It was also kind of a reaction as well. Okay, I think I want to go in some quick questions. ⁓ You did answer a bit of it, but one overhyped and one underhyped province or city that you think people are not noticing enough outside of China.

Tom Nunlist (41:28)

Yeah, again, would say,

Yeah, they’re not really as specific over under Hype City that I can think of. But yeah, I would say go back, double down on the point of like, you know, look at where different specific hubs are, right? So right now, you know, especially the US talking about AI development sort of in general, right? Like the rush to AGI, you know, so on and so forth, right? The AI plus plan is about doing things in the real world, right? So I think where a lot of like really fascinating stuff is going to happen is where those real world

things are in China, right? So like where we have many filtering use cases actually emerge and that’s going to be sort of all over the country.

Grace Shao (42:02)

Right.

Right, like CN maybe for renewables, but like Hefei for you say auto, and then like even like Baoding and Hebei for like auto. You get at least like second, third year cities that are just like actually relevant, but only if you’re in the sector, you would know, right? And that’s a really interesting take. So what is one metric that a policy analyst like yourself should be really tracking or focusing more on?

instead of just, you know, maybe what we’re seeing on the headline is like, you know, this crazy chase for like benchmark frontier technology, frontier of LLM benchmarking. How do we actually track or judge real AI diffusion in the economy?

Tom Nunlist (42:48)

would say it’s probably more along the lines of traditional measures, So penetration, productivity, profitability, wage and efficiency growth. Again, the emergence of those scenarios, Are people actually out there using it in the real world? So I think it’s look for those traditional.

tangible things, right? Again, I mentioned that Chinese consumers tend to not want to pay for consumer-grade AI tools. If that’s something that changes, right? If they’re good enough where people are willing to pay for it, wow, maybe that would be an enormous indicator.

Grace Shao (43:16)

Yeah.

I don’t think anyone’s gonna want to pay for like, you know, consumer app. The culture, right? Like no one wants to pay. I don’t know, I switched my brain on and off when I use like Western apps versus Chinese apps. And when I’m on a Chinese app, they pop up, they’re like, pay for premium. I don’t want that filter anymore. I don’t need this sticker anymore. I’m like, I’m not paying. You just have a different mentality, right? Because you do get too many goodies for free already. It’s very...

Tom Nunlist (43:27)

I think he wants to go to pay for a I know.

Yeah.

Yeah.

Grace Shao (43:51)

It’s very hard, think. The barrier is very high. The threshold. I have one last question for you. And it’s a question I ask everyone that comes on to differentiate understanding, which is what is an unconventional view you hold? And this could be about work or something in life, you know? But what is something that you think about and you’re like, oh, maybe I don’t say this out loud, or maybe this is quite different from what my peers think?

Tom Nunlist (44:13)

What was an unconventional view I hold?

I’ll go one with topic specific here. That’s because it’s come up recently in ⁓ fights I get into, Twitter fights I get into with people. There is this interesting and I think not totally off the mark concern with AI that it’s gonna basically make us all dumber. Students are gonna outsource learning to AI. There was a case study that did the rounds about doctors using AI tools to help them spot

certain types of cancer got worse at it, know, like after relying on the tool. And that’s a real concern. I think it’s something that, you know, there’s some red flags that seem to say that that might actually be happening. But I think the real problem might be a bit more nuanced than that. think it might, my hypothesis is that it will create something like a ⁓ skills or performance gap.

between different parts of the population and exaggerate it. So, whereas some groups of people might become reliant on it and become de-skilled in their job, definitely. And then in some cases, that might be what we wanna happen. I we don’t want everybody, I mean, that’s sort of the promise, not have to do certain boring things. But I think for a smaller portion of the population, it is gonna be a massive learning and development.

accelerator, right, to really help you to get good and improve. And so, you know, I mean, beyond, you know, whether or not I’m right, I don’t know I’m right, it’s just a bit of a guess. You know, I’m wondering where that gap kind of might be and how large it’ll be, right? So is it going to be 90 % of people get dumber and 10 % of people become super learners? You know, or is it, you know, somewhere in between?

That’s my unconventional view. It’s gonna create a skills disparity.

Grace Shao (46:04)

Yeah, I actually kind of agree with I think it would make people who are relying on it for skill set like vocational skill almost like just the art of, know, not art, but the skill or ability to write a press release or draft a basic news piece or you know, build a DCF model or you know, do some quick basic research that might become dumber in the sense that you don’t know how to

do it in a traditional way. But I think the arguments also like say 34 years ago, people are like, you have the internet now. You don’t even know how to use a library anymore, which I think our generation honestly, I don’t really know how to use the library very well. Like I go on my loss. I don’t know how, you know, how to find books essentially from alphabetical order and, you know, like finding the topics, but we do learn how to find more information in some sense, right?

But I think to your point of like, ⁓ it will help people learn a lot faster, but it will require a new kind of skillset, is like, you can access all this information, can you decipher it? Can you dissect it? Can you actually pick out what is correct? What is actually relevant? Because there’s so much noise and clutter, which is kind of similar again to our generation where we had to use the internet to find information, Versus like our parents generation had to like walk into the library and just like.

Grace Shao (47:19)

go through like 10 books, right? ⁓ But that’s super interesting. Thank you, Tom. Really, really appreciate your time. This was super insightful. It was really helpful for me to even learn about how to understand how policy was made in China, how it might affect businesses and investors. And yeah, this was just super insightful and a lovely conversation.

Tom Nunlist (47:21)

Yeah. Yeah.

Yeah, thank you, really. It was really lovely to be on the pod.

AI Proem is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Joining me today is Natalia Cote-Munoz, a policy strategist, writer, and International Strategy Forum (ISF) Fellow— a program by Schmidt Futures that supports rising leaders at the intersection of geopolitics, technology, and public service. Natalia has served in the U.S. State Department, leading foreign policy think tanks and crisis diplomacy roles. She is a graduate of the Harvard Kennedy School and speaks English, Spanish, and Mandarin, among other languages.

In this conversation, we discuss Natalia’s unique upbringing as a third-culture kid, her experiences in tech diplomacy, and the evolution of US-China relations in the tech sector. Natalia reflects on her recent return to Beijing after a decade, sharing insights on the rapid technological advancements in China, particularly in AI and digital payments.

We also discuss her observations of how diplomats are trained as an international relations teacher at China’ Foreign Affairs University, how AI cannot be replacing humans in diplomacy, her embrace of AI in productivity and creativity work while she was experiencing a concussion, and lastly her unconventional belief about the societal views on Labubus, highlighting the cultural differences in perceptions of childishness and professionalism.

In today’s world, there’s no shortage of information. Knowledge is abundant, perspectives are everywhere. But true insight doesn’t come from access alone—it comes from differentiated understanding. It’s the ability to piece together scattered signals, cut through the noise and clutter, and form a clear, original perspective on a situation, a trend, a business, or a person. That’s what makes understanding powerful.

Every episode, I bring in a guest with a unique point of view on a critical matter, phenomenon, or business trend—someone who can help us see things differently.

For more information on the podcast series, see here.

Chapters

01:30 A Third Culture Kid’s Journey

04:21 Evolution of US Tech Diplomacy

11:32 Reflections on Beijing After a Decade

17:54 Exploring the Red App and AI Conversations - Doomism vs. Optimism

28:35 Education and Talent Development in AI

38:20 Exploring Student Aspirations in International Affairs

40:33 The Role of International Faculty in Education

41:56 STEM vs. Liberal Arts: Educational Mindsets

47:41 AI as a Productivity Partner: A Personal Journey

56:24 AI in Diplomacy: The Human Element

01:01:26 Legitimacy in AI: Who Builds It Matters?

01:02:00 Cultural Perspectives on Professionalism

AI Proem is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.