Today, I’m joined by Matt Sheehan who writes this insightful newsletter. Matt is a senior fellow in the Asia Program at the Carnegie Endowment for International Peace. He researches China’s AI ecosystem, Chinese tech policy, and how technology shapes the country’s political economy.

Matt lived and worked in China from 2010 to 2016 and later led China tech research at the Paulson Institute’s MacroPolo. He’s the author of The Transpacific Experiment. He speaks Mandarin, and he turns complex policy into plain English.

In this episode, he helps us understand China’s AI governance, about how Beijing is thinking through the social and political consequences of rapid AI adoption. We focus especially on a shift that became more visible in early 2025: rising concern inside China’s policy community about AI’s impact on jobs, worker anxiety, and social stability.

Matt explains why China’s AI labor question is different from the Western debate. We also discuss how the Chinese government is trying to balance support for technological progress with the need to manage public anxiety, clarify labor rules, and avoid social instability as AI becomes more deeply embedded in the economy.

He broke down the myths, explained the jargon, and the regulatory bodies in China. Our conversation started slow, but it became very, very heavy, what they call 干货满满 substance heavy. Also, a shoutout to Nathan Lambert’s work in helping us better understand the open-source ecosystem and Rui Ma’s for helping us understand investing in China AI!

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. Season two will host a series of guests from early-stage investing, as well as builders, founders, and product managers.

For more information on the podcast series, see here.

To find the previous episodes of Differentiated Understanding, see here.

Chapters

00:00 Introduction to AI Policy in China

03:10 Matt Sheehan’s Journey into Chinese Tech Policy

05:55 Shifting Perspectives on AI and Labor

09:02 Public Concerns Over Job Security and Government Responses

15:09 Education and AI: Preparing for the Future

17:50 Regulatory Landscape of AI in China

34:00 Navigating China’s AI Regulatory Landscape

40:58 Misconceptions About Chinese AI and Government Funding

43:57 Understanding AI Safety and Security in China

52:03 Global AI Governance: Cooperation or Parallel Paths?

AI-generated transcript

Grace Shao (00:01)

Hi Matt, thank you so much for joining us today. I’m so, so happy to finally have you on the pod for people who are listening. We’ve been trying to make this happen for like six months, but between us, there are like three little children running around with a bunch of viruses and have just not been able to make this happen. I’m really excited. ⁓ A few months ago, what really caught my attention about your work again is that you shared something on WeChat saying you were dissecting the new Chinese AI safety paper, like the big national one. ⁓like verbatim in Chinese. And I was like, wow, this is extremely impressive. It’s not an easy task. I commend you for doing that. So I really wanted you to help us understand the nuances of the AI policy world, especially how people are perceiving AI in China. I think there’s more more interest in how China’s governing AI ⁓ while we were hearing the backdrop of how the Chinese government is trying to push on AI diffusion, right? And then on top of all of this, like where areas where China’s AI governance seem to be leading, because in many ways it seems like

China’s AI regulators are much faster to respond to how fast technology is evolving. But to start, we would love to hear about your personal story. Tell us about how you ended up studying China, studying Chinese tech policy. We met in Beijing years ago, maybe a decade ago. ⁓ Yeah, so tell us about that.

Matt Sheehan (01:11)

Sure.

Yeah.

Yeah, sure. Sort of stumbled into China stuff. I hadn’t taken Chinese or really knew anything about China until about halfway through college when I ended up getting a summer job in Beijing. I was just kind of like instantly fascinated and knew I wanted to move back there after I graduated. So took a little bit of Chinese my senior year, moved to Xi’an, taught English, kind of followed what at the time was a very like typical

know, trajectory of like, go there, teach English and then go study Chinese at university and then get a job and get a slightly better job. And eventually I was able to kind of wiggle my way into journalism. And so I was a China correspondent for a publication called The World Post at the time. And that took me up. was there from 2010 to 2016. So kind of like the hinge period before and after she came to power. Pretty interesting thing to see. And

When I moved back to California in 2016, I started working on a book about China-California ties. I’m from California and this was like the period of kind of explosion in cross-border investment and Chinese students come into California in the Silicon Valley-China relationship getting even more like twisted and complicated. China-Hollywood. So I wrote a book about that and as I was doing it, the kind of the tech section, the China-Silicon Valley, China-U.S. tech connections kept growing bigger and bigger and I ended up

working a little bit with Kai-Fu Lee on his book, AI Superpowers, which was kind of my turn from like, it was like all things China, China, California, China, Silicon Valley, China AI. And since 2017, I’ve been working almost exclusively on AI issues in China. Maybe the first three years of that, like 2017 to 2020, was very focused on comparative capabilities. This was kind of a period right after the National AI Plan in China when there’s a big explosion in activity. And I think...

This is kind of was like the first time America kind of got freaked out about Chinese AI capabilities. And so I spent a few years being like, okay, let’s try to like ground these assessments in some data. Let’s get like an actual grounded sense of where the countries are with each other. ⁓ And then starting in 2021, I sort of turned into focusing on Chinese AI governance, Chinese AI regulations. That’s when they first started rolling out their regulations or recommendation algorithms and sort of deep fakes. And I was kind of making a bet that I think

If China continues to be at or near the frontier of AI, then how they choose to regulate it domestically is going to have huge implications for China’s own ecosystem. And then it’s going to really ripple out internationally on safety, security, growth, all this stuff. So I spent the last, now it’s like five years, ⁓ just deep in the weeds of Chinese AI policy and regulation.

Grace Shao (04:04)

Oh, great. I think I definitely want to double click on all the algorithm security and the kind of, you coined the answer versus what’s called security and versus what’s the other Chinese word? Yes, yes.

Matt Sheehan (04:17)

Anshun safety security. ⁓

Jeff Ding was talking about this very early on, but yeah, it’s a, it’s a constant thing that we have to negotiate for people who don’t know it’s the word Chinese, the Chinese word Anshun ⁓ means both safety and security. whenever you’re kind of translating documents on this front, you have to know, you talking about AI safety, which is kind of a different thing versus AI security, right. I think both you and Jeff definitely are some of the more nuanced scholars I follow. And I do want to kind of double click on that later on. But to start, I think I want to talk about something that’s top of mind for a lot of people. You just wrote a piece that you said was not super serious. It was just your scattered thinking put together on subset. I thought it was very well written about the growing anxiety around potential job losses. ⁓

your perspective is that you know there are more and more people voicing this kind of concern and I wanted to hear a perspective on that and I kind of wanted to share a bit of my different share my different perspective on this and what I’m hearing on the ground and kind of have a conversation around that as well. Yeah why don’t you start with sharing like what you found yeah

Matt Sheehan (05:22)

Yeah, sounds great. Yeah. So, I’m not an AI and labor person. That’s not been my focus for a long time, but I’ve been monitoring it for a long time and just lightly. And starting around, I guess it was early 2025, I just started to hear a lot more out of the Chinese policy community about worries about AI’s impact on labor and jobs. And this was kind of a surprise to me because ⁓ just a little bit prior to this, say early 2024,

I had, I sometimes ⁓ in my job, I run these kind of like informal surveys or almost like a, what do call it, focus group of American and Chinese AI policy people and asking them like, you how would you rank these different risks? How concerned are you about ⁓ job risks versus privacy versus military AI? And we have both sides that like rank the risks and then talk about the results. And when I ran one of these in early 2024, it was very striking that the Chinese side

I think it was at the time we had seven different risks and the Chinese side ranked labor impacts a second to last as six out of seven. And so my sort of baseline was like, OK, for a variety of reasons, this isn’t really too on the radar of China’s ⁓ policy community or wider policy community. And then starting around early 2025, some of those same people who I had been talking to about this before had really changed their thinking. They were saying that there was a big change in thinking within

China, maybe especially within China’s of policy and government circles, but then I think also a little bit wider. And so that sort of sparked my curiosity. And for the past, now it’s like over a year, I’ve been just sort of tracking when does this AI and labor question show up in state media? When does it show up in kind of the online discourse? When does it show up in policy documents? And sort of the TLDR is like, I think this is really, really ramped up a lot. Over the past 18 months. It’s been maybe the single biggest change in how China perceives different sort of risks as it relates to AI. And I think I’m looking forward to sort of discussing how maybe like the policy world or the government’s perception of this differs from ordinary people or certain categories of people. ⁓ But I think it, from my perspective, it’s sort of it’s infused into both. think there’s been a fair amount of public concern.

the policy community picks up on that and they want to both respond to like the actual problem, know, actual job losses, but they also really want to respond to people worrying about job losses. That’s kind of maybe the thing that actually made me write this piece now was discovering an interesting piece in state media. I think it was in science and technology daily. That was the headline was like ⁓ AI must be controllable, but people’s ⁓ anxiety about AI must also be controlled.

And it was all about how sort of OpenClaw has triggered a lot of anxiety and a lot of people about, are they going to get replaced? You need to be building your own AI agent in order to not be left behind. And they’re sort of trying to tamp down those concerns in a few ways. So that’s what sparked the piece itself.

Grace Shao (08:33)

Yeah, I think definitely what you saw and you wrote about is like definitely kind of playing out in the China AI policy ecosystem that I see as well. And I think for sure, the open-claw frenzy have kind of opened the eyes to lot of the even average people what AI could potentially do. However, I guess my argument, not against it, but it’s just like, you know, we kind of cite each other’s work on subset. But my point was kind of saying, you know, this is a reflection of a relatively elite group of people end of the day, because the knowledge work economy in China end of the day is only like only 30 % of the workforce actually are the knowledge working economy. And end of the day, even though it’s 30%, because China has such a huge population, the mass, the sheer scale, it feels really large. However, I want to bring it back to the idea that like anyone who’s lived in China understands that the government’s like top top priority really is about social stability which leads to what they call social harmony, right? And I just think that, you know, the rising anxiety of job control a lot of times maybe is because there’s a fear of if there’s a lot of disruption to jobs then people will lead to social unrest which obviously gets a bit more sensitive but you know, a lot of what they do comes from that I guess thinking so I agree with you top down definitely have to understand what’s happening with technology and how advanced AI has become in 18 months.

⁓ have given them kind of, I guess even fear mongered a little bit internally, right? ⁓ But the nuance here is that I think ⁓ the rest of the 70 % of the Chinese workforce actually don’t work in anything structured that we know. I think even probably even 80%, you know, people in China, they most of them are actually like, you know, service providers ⁓ from, you know, rural areas and urban areas. A lot of people work in factories, even the entrepreneurs, right? They run like say hospitals, clinics, factories, bottle cleaning, like factories, whatever, right? ⁓ Car logistic rental businesses, these people aren’t actually ⁓ trained in the way that maybe the West by default think they are. They actually just run it from a grassroots way and they don’t have very, very streamlined processes. They don’t have documentation. They don’t run like what we think a corporate has run. So in that sense, I think it’s very hard for AI to replace any of their workflow.

because it’s actually not a, we can’t really provide context and a lot of things, business is done is through one C, is through a wink, through a look, through a gesture, through, you know. So a lot of that, I think, in fact, will be harder to replace than even maybe some of the more mature businesses in the West where there are structured processes and everything. So that’s kind of my, I guess, a more nuance, I think, push on that. Yeah, wonder what you think of it.

Matt Sheehan (11:20)

Yeah. Lots of thoughts. And I think the sort of the fundamental distinction that you’re pointing at is very valid in that like, you know, companies in the West, in the United States, they’ve been like big companies have been running sort of digital databases for decades. They have like decades worth of data. They have pretty advanced like enterprise software. It’s just a much more I want to say something like a bias, but it’s a little more like put together sort of official structured

⁓ technological backend and not just technological, but like a process backend. Whereas in China, it’s just things have developed really quickly. A lot of it’s on the fly. lot of it is, know, enterprise software is just not, there’s not really a market for that in China in the same way. It’s a lot of stuff is pirated or they’re just not digitized in the same way. And it’s actually very interesting. This is in kind of the early days of the like China, US who’s ahead. ⁓ know, a lot of the debate focused on data.

Matt Sheehan (12:18)

And there was this idea of China has a billion people, so it must be this huge advantage in data. But my pushback on that was always kind of what you’re arguing, is like the US actually has very structured data, and it’s owned by corporations, it’s deployed by them, they’re already doing type of sort of lower end market intelligence type stuff. ⁓ So I think that that sort of backdrop is very real. think ⁓ maybe from there I’d like differentiate out to potential risks or debates. And it’s kind of what you were pointing out as well. There’s like the actual question of how many jobs are going to be impacted. How many people are, what is it going to do to people’s wages? What’s it going to do to aggregate employment? And then there’s the question of like, ⁓ how do people think about that? What are the fears due to sort of the Chinese social stability, even if the things haven’t manifested. So I think separating those out, definitely the government is... ⁓

their sort of initial response is related to public worry about this. So in the piece, I detailed the way that ⁓ sort of a robo taxi incident in Wuhan was in many ways the spark that really like ramped up the government thinking on this. And this is something that I heard from a couple of different Chinese policy people who both pointed to this incident, which I had totally missed at the time and wasn’t like major international news, but that had a big impact. And basically what it was is that

Baidu was rolling out its sort of fully autonomous robot taxis throughout the city of Wuhan. There was this kind of like public letter, ⁓ open letter released by a taxi company that was kind of railing against, you know, both ride hailing platforms and autonomous vehicles as, you know, stealing the iron rice bowl or just smashing the rice bowl of taxi drivers and of companies. And even though it was a kind of like a small thing, a couple of days later, a Baidu taxi actually hit pedestrian, I don’t think they were seriously injured, but it kind of fed into this overall, a big kind of online reaction and discussion about like, what’s going on with AI? Is it going to take people’s jobs? And it, it’s one of those things, it’s funny to explain to people because it sounds like nothing, but it did lead to a pretty significant ⁓ imprint on the way that the Chinese government is thinking about it. So it was coming from, in many ways, public discussion of it. Like the discussion was happening online. This discussion might be happening among, you know, elites.

chronically online people. ⁓ But it’s something that the government definitely picks up on. So I that’s one element. They’re worried about the worries and they want to, like among their sort of policy reactions in a ways, thus recently has been sort of directing platforms to say like, you kind of need to tamp down these articles or these viral videos that are telling everybody, like if you don’t adopt open claw, you’re going to be left behind. There’s been this kind of rash, both in China and here in the US of like,

you know, kind of like fear mongering people into clicking and taking your course on building agents or just subscribing, whatever. And one thing the government is doing is telling you like, chill on that. Like, don’t be putting that narrative out there. ⁓ And so that’s part of this kind of like public opinion management thing. In terms of the actual impact on jobs and who will it hit?

It’s a huge open question that I personally have gone back and forth on for years. I first kind of did a deep dive on this way back in 2017 when everything was still so speculative. At the time, was pretty not worried in part because of the reason you described it. I’m like, there’s just a lot of friction. There’s just so much friction in this economy. And just because an AI system can theoretically execute a test doesn’t mean it’s taking a person’s job. And for me personally, my thinking on this has changed a lot in the last

year to 18 months, mostly because of how capable agents have proven to be. I expected agents to essentially be hitting a lot more roadblocks while they’re being deployed online. They haven’t been. They’ve been operating much smoother, or just they’re more relentless, and they can break through these bottlenecks. ⁓ It’s interesting that in China, the inciting incident was not about white-collar workers. It was about taxi drivers. ⁓

I don’t know this as a fact, but if I had to guess, I would guess that a much larger portion of the Chinese population’s job is driving a car or driving a scooter or something like that. And that maybe that’s a vulnerability that they might face depending on how self-driving vehicles roll out or delivery robots and stuff like that. The knowledge workers, yeah, it’s such a messy and unclear thing, but I think the government has at least started to take it seriously because it’s not, their policy responses are not just this like,

public opinion management stuff. They’re also talking about, ⁓ like one of the more interesting pieces that I highlighted is, and maybe the most concrete thing they’ve done so far is, ⁓ according to Chinese labor law, there’s sort of reasons why you can and cannot fire a person. There’s like legitimate and illegal reasons to fire someone. And when someone is fired and they object, this gets taken to like a labor law mediation ⁓ body that’s under the Ministry of Human Resources and...

forget what the second part social ⁓ security. ⁓ Yeah, Ren Li, Ziyuan, Shouhui Bao Zhang. Yeah, that’s what it is. ⁓ And in the last year, one of the things that really made a lot of made kind of a big splash is that those mediation bodies declared and it was echoed in like the biggest state media that saying that you replace someone with AI that AI can now do this person’s job is not a legitimate reason to fire someone and those people have to be reinstituted into their jobs.

That’s a concrete policy thing that’s actually directed quite clearly at like actual impacts. ⁓ Is it going to work? I really don’t know. It might just be a little bit of friction and, you know, maybe China’s kind of doing what it always does, which is like, we’ll figure this out. We’ll kind of muddle through this. We’ll put some friction here. We’ll grow a little more here. But I think the concerns are real, whether they bear out, ⁓ whether they hit faster in the United States or China, which country is better positioned to sort of roll out a more redistributive welfare system. think these are all open questions, but I think the concerns at least are real.

Grace Shao (18:24)

Yeah, and I think you hit something that I feel like it’s being kind of missed in the headlines, which is the government actually cares more about the general mass, which are the people who are driving the scooters and the like the DDS cars more than the knowledge workers, which is kind of different from the Western kind of conversation right now, where a lot of the whether it’s fact, frankly, the power that can lobby and the power that the voice that have the voice are all really concentrated in.

the white collar elite jobs that are very much concentrated in Silicon Valley and whatnot, right? And I think it really reminds me of the time when, you know, during the Hulianmang Shidae, like the internet era, you know, like the big tech only really got clamped down when the average consumers felt like they were really being pushed to R-Shrine Egypt 2-1. So that’s when they had to add the monopoly to probes. And then soon after, only maybe two years after the probes happened, there was a common prosperity rollout, which basically all the big tech in some capacity had to like showcase that they had a CSR aspect to them. I think this is something that we don’t really see in the US as much with all the big techs, because it’s kind of like they’re doing what they need to do. They have their profit driven interests. And then of course, everyone has a CSR, but it’s not really allegiance to the government CSR mission. It’s more like, we believe in ESG. We believe in climate. Amazon is going to have some like carbon footprint reduction plan, right? Whereas like the common prosperity thing rolled out. ⁓

you know, it kind of died on its own, like no one really talks about it anymore. However, during that phase, when it did get rolled out, it was like an understanding where, OK, if the government wants the, frankly, the poor or the middle lower class to feel protected, then you as a very large ⁓ moneymaker in the economy need to showcase that you are somehow ⁓ part of this kind of support. So I wonder how this will play out for the big tech in China when, the job protection policies really get rolled out in practice, like what you’ve mentioned. And obviously they can’t really say, you’re being replaced by AI. At least there’s that superficial guardrail there, I think.

Matt Sheehan (20:24)

Yeah, I think the sort of the political economy of these questions is going to be super interesting in both countries. know, essentially like business in many ways, it inverts the sort of technological impacts on employment that have been around for so long. Normally, like greater technologies integrated into the workforce, it hits sort of people working maybe low end manufacturing jobs or jobs that would be considered sort of repetitive and, you know, quote unquote, low skilled jobs, even if they’re not. ⁓

And, you know, in the United States, we’ve seen like three, four decades of this. And the people who are concerned about that basically didn’t get hurt because they are not, like you say, part of these influential classes. AI is going to, yeah, in the United States is going to be very different. This is going to be the first time that you have the way that my sort of mental model for it is like, if you’re a senator and you have kids or nieces and nephews or your friends, kids like what, what are their problems?

And like how close do those feel to you? And you know, if you’re a 60 year old senator and you’ve got like a 23 year old niece and she just graduated from college and she got a degree in, you know, something that’s like is legitimately employable normally like in marketing or something like that, and those jobs just aren’t there, that’s just going to feel very close to home for people in power in the U.S. in the ways that it hasn’t felt in past waves of technology impacts. In China, I...

partially agree with what you’re saying, but I think there is also going to be a significant element of this sort of the same dynamic as the United States. mean, yes, common, you know, Xi, common prosperity. He’s focused a lot on sort of eliminating extreme poverty and, you know, the CCP, it’s in its bones that like the rural, the working class are in many ways kind of the long term support base of them. But I mean, also if you look back at Chinese history, like a lot of the biggest and for the government most dangerous protest movements came out of elite schools, came out of students at elite schools who ⁓ either couldn’t find jobs or were facing inflation issues or had, for ⁓ ideological reasons. I think that stuff does hit close to home. think some of those same dynamics, if you’re a deputy director at the National Development and Reform Commission.

your family, the people that are close to you, are going to be the type of knowledge workers that are going to be impacted by this. And I think that just can’t help but kind of like compress in on the thinking on this. ⁓ You know, how AIs can... Yeah, yeah, and like how...

Grace Shao (22:58)

Yeah.

everything becomes personal in the end. Like in the end, it’s like

politics is still personal. Yeah. Sorry.

Matt Sheehan (23:08)

Politics

is personal and yeah, mean, like cities are where social instability is the most dangerous. Like cities are where people gather and you can have potentially dangerous incidents. These are the people who are very online and are sort of sparking or leading the conversation as much as that can be controlled and manipulated via censorship regimes or public opinion guidance. Like these people are gonna be vocal. yeah, I think if I was at the CSPI, I’d be concerned about

Grace Shao (23:39)

I want to kind of go into on education. Like you kind of touched on it, right? Like, you know, there’s been draft rules about children’s interaction with AI in China as well. There seems to be more guidance and obviously concerns around that ⁓ from at least from the top down ⁓ about their mental state, their dependency, or even what constitutes as an AI companion, how we should draw the line on that. We know like

famously a couple years ago, China installed this rule where like, you know, kids under 16 cannot actually play online games on their own without the parents consent. However, that you know, there’s obviously loopholes in practice. But again, it goes back to there are, you know, rules and laws in place to try to protect minors. How do you view all of this? ⁓ Because in the with the backdrop of China trying really hard to diffuse AI into the real economy. And then there is this pushback like you just mentioned on

concerns about AI taking jobs. I feel like there’s also almost like a ironic kind of contradiction happening where, you know, Tiger moms are like, okay, now we don’t need to learn math, we know how to learn AI. And Tiger moms are like saying, how do we optimize getting into, I don’t know, Harvard with AI’s help? And how do we get AI into the education, education, ASAP? I mean, honestly, we don’t even know what the education system might look like in like two decades.

from our kids, but at this point, seems like there is like embrace. I don’t know. How do feel about that?

Matt Sheehan (25:13)

Yeah, a couple strands there. One just on the sort of regulatory side, like this is a long term strand in Chinese tech policy and tech regulation. They always put a pretty heavy emphasis on like how are kids using technology. They have, ⁓ they sort of mandated having like a minors mode on various ⁓ apps. ⁓ This is the regulation I think that you’re referring to as the newly passed. ⁓

I translate as anthropomorphic AI. That’s the word that’s the official translation. ⁓ So it basically means AI that, you know, behaves like a human. This could include AI companions that are, you know, literally like a character pretending to be your friend. They could also include, you know, the way that people interact with chat, GBT or Kimmy or whatever, you know, the phenomenon of having AI boyfriends and girlfriends and all this stuff. So there was a new regulation on this that was just finalized, I think, last week and

It has some protections for everybody, specifically around ⁓ self-harm, addiction, and stuff like that. But it has really ramped up protections for minors and for elderly users. So there’s all these kind of specific add-on requirements. For ⁓ minors, it involves permission from parents. Parents can review at least some. They can set limitations on how the child uses the system. They can review.

conversation that might have got toned down a little bit in the new version. ⁓ But I think, this is many ways it’s the same concern that surfaces in the US and elsewhere, like California just passed a just passed last year, passed a similar regulation on AI chat bots that I think also had believe it had extra protections in there for kids ⁓ on the education side of things. I guess there’s a couple of things. One, there’s just like the yeah, you say the tiger mom’s like this is ⁓

It’s a booming industry of like, I’m going to teach your, you know, four year old AI so that they can use it because this is going to be how they get a job and how they get into school and how they get a job. ⁓ you know, a lot of it is bogus. Maybe most of it is bogus, but it’s very attractive to parents who have grown up in a really, really cutthroat competitive education system where you’re looking for every single edge that you can find.

And so that’s, that’s a piece of it on the, from the policy side, they have both sort of AI and education policies, AI plus education policies that they’re pushing in a bunch of ways. I have some friends who are working with teachers over there who are described to me pretty like sophisticated and interesting ways. The teacher that are using AI to lesson plan, to create like really interesting games that keep the kids engaged and learning stuff like that. So you have those, and then on the labor.

the labor side of things, they’re also viewing AI, they’re also viewing education as something of an antidote ⁓ to AI fuel job disruption. This is in the, I in the five year version, it is in the five year plan, it’s in the AI plus plan, it’s in a few other places where they say, we’re really gonna prioritize lifelong education. So maybe you used to be an accountant, you lost that job and ⁓ you’re gonna retrain as something else. ⁓

which I think is a good, you know, it’s a good attitude to have. If you’re a person, you should always, I’m always trying to learn, you know, books. Um, think they’re great, but I don’t know if at a totally like a, you know, macro population, I know if you’re going to get 500 million people to be constantly staying one step ahead of AI in terms of what jobs it can do now. I mean, a lot of the things that we would have told you go back like two, three years and say, what jobs are going to be disrupted by AI? A lot of the recommendations would have been totally backwards.

People would have thought that, ⁓ coding jobs are great. jobs involving creativity, ⁓ illustration, ⁓ stuff like that. AI can’t do those things. It can’t be creative in that way. And it’s like, that’s actually kind of what it’s best at now in some ways. I mean, you can argue about the level of creativity, but like generation of content, generation of images, videos, language. So I’d say it’s a piece of the Chinese sort of response on labor concerns.

a fad, but maybe like a ⁓ useful fad within like the sort of education industry. But I’m a little bit skeptical of this as like ⁓ an actual antidote to the disruption that I at least imagine is coming.

Grace Shao (29:49)

Yeah, it’ll be really hard to be like upskilling, like re-skilling like hundreds of millions of people. Like, it’s just, you don’t even have the capacity to do so if there’s actually mass disruption. alright. ⁓

Matt Sheehan (30:00)

mean, this was always the response, in the United States on coal miners. We’re going to teach them to code. everybody, all these manufacturing workers, we’ll offer like a job retraining program. I’m like, ⁓ maybe, yeah.

Grace Shao (30:05)

Yeah.

I’ll take generations. I’ll take generations for things to shift, know, resources to shift, people’s mentality shift, you know, for a while, like, when many, when remember the first wave of like, a basic rural kids no longer wanted to work in factories and wanted to go to urban cities, there was a surplus essentially service providers and then like everyone eventually became a DD driver or a food delivery man and then

Now we’re seeing a reshuffle in that population again, where people want to move back to their rural, cities. so I think based on how society is evolving, opportunities will arise without even us realizing anything, hopefully in the best case scenario, where people will find opportunities to reskill. ⁓ But I want to talk about something that’s a bit

Grace Shao (30:57)

I guess not heavy, but actually not many people understand, even including myself. So you really look at the government and the policy structure of ⁓ China’s regulators in the cybersecurity space and whatnot. ⁓ There’s so many players. There’s the CAC, then there’s the NDRC, there’s the MMIT. I can keep on naming acronyms, but can you give us a really, really quick high level understanding of who’s regulating whom? ⁓

how do they actually work with each other and are their KPIs aligned before we get into more about how you know, how China’s policy is shaping the technology and AI ecosystem.

Matt Sheehan (31:36)

Yeah, maybe I’ll do it. ⁓ I’ll introduce a couple of the players and I’ll do it somewhat chronologically in terms of like when have they become important or rise and fall in importance. So AI policy, like the first really big policy document was the 2017 National AI Plan. It was released by the State Council, effectively sort of China’s cabinet, sort of the highest level of government. But ⁓ people who are in the know say that that was largely sort of drafted and pushed by the Ministry of Science and Technology. So this is

really like the policy wave of like 2017 to 2020 more or less. And it’s the Ministry of Science and Technology and it’s also the Ministry of Industry and Information Technology, MIIT. So these are really the organizations whose job it is to promote science, promote innovation, and MIIT is more of like the industrial applications of the technology. So they were kind of in the driver’s seat in that period of time. They were the most relevant actors. They were the ones who were driving real activity.

starting in 2020, 2021, you had the CAC, the cyberspace administration of China, really like come to the center and become the most important actor in AI policy. The CAC, it’s basically the internet regulator. It was created in 2014. It was largely created to kind of like get the Chinese internet under control from a sort of political content ideology perspective. They’re connected to the Ministry of Prop, or the propaganda department.

publicity, as they say now. ⁓ So from 2021 through 2023, the CAC was the one rolling out these binding regulations on recommendation algorithms, on deepfakes, on generative AI. And these are the regulations that actually force companies to do things. They actually force companies to register their models, to do pre-deployment testing, at this point to label AI-generated images in different contexts.

There’s for that 2017 to 2020. It’s kind like the go-go period. Let’s just like push this industry forward. You have the Ministry of Science Technology, MIIT. And then from 2021 to 2023, it’s really the CAC. This corresponds roughly with the tech crackdown of 2020 through the end of 2022. That was a period when the CAC, the CAC is kind of at least historically, it’s kind like the bad cop of tech policy. They’re the ones who are like telling companies like come in and drink tea and we’ll tell you what you’re doing wrong or, finding companies in different ways. Cyberspace Administration of China, yeah, CAC. ⁓ Some people call it CAC. ⁓ And then sort of one of the more significant changes from 2023 to now is the rise of the NDRC, the National Development and Reform Commission, Chinese Fagawei. ⁓ And they are a macroeconomic regulator. They are like what grew out of the sort of state planning apparatus. And they’re

Grace Shao (34:01)

This is a cyberspace administration of China, right?

Matt Sheehan (34:30)

really powerful, they’re kind of a super, super ministry within the bureaucracy, but they’re not sort of directly, there aren’t that many direct connections to AI. They deal with, they deal a lot with money. They have money to give out for projects that funnels into compute projects and stuff like that. ⁓ But they wouldn’t be like who you would think of as the go-to AI regulator. What I was told and what I feel pretty confident ⁓ is what happened is that in some time in, I think, 2023, maybe mid to late 2023 and then into 2024, the top leadership in China essentially said, hey, we need a little more balance in our AI policy. The last three years it’s been led by the CAC. They’re kind of a bad cop. They’re really focused on controlling the technology, controlling the sort of output, the content, the ideology from it. And that is important. That’s kind of their first priority. But we need to rebalance this a little bit. We need to move out of our total tech crackdown era. And now we realize like our economy isn’t doing great.

We realized we’re behind the US after CHAT GPT came out, and we need to balance this out. And so they empowered the NDRC to be a of a coordinator across AI policy, someone who is intended to take the input from the various ministries, from Ministry of Science and Technology, MIIT, CAC, and to try to make it little more coherent and balanced. And so that’s kind of the role that they have played for the past few years. The details of how that works out are

shrouded in secrecy, you know, you hear little tidbits here and there. But there have been like visible manifestations of it. They had not, when they released these regulations, usually there’s a sort of a lead regulator on it or a lead policy document person on it. And then various other ministries, they co-sign it and they’re like listed below. NDRC hadn’t been on any regulations prior to 2023. And then starting in 2023, they were listed second as like the second sort of most important organ.

policy body on these things. essentially we have this kind of like 2017 to 2020 is this like go-go period. Let’s diffuse. Let’s push the technology. Let’s push innovation. 2020 to 2023 is this more constrictive crackdown. Let’s build the regulatory infrastructure for things. And then 2023 to today is just like, let’s balance this out. Let’s not be purely focused on the content and ideology concerns. Let’s also be thinking about development. Let’s be thinking about employment. The NDRC is actually allegedly one of the groups that is very concerned about the employment impacts. you know, tons more details that I will love to go in on, but maybe that’s a starting point.

Grace Shao (37:06)

No, I think it’s super, super helpful. I just understand the nuances of like what their actual KPIs even are and like, you know, who does what, how they work together. I think that’s really helpful for lot of listeners and even investors who are trying to follow the space and just confused by acronyms. But help me understand now, like you say that 2023 to now essentially is in the same kind of era. However, I feel like at least from the capital market perspective, you know, the last year might have seen a bit of a

shift again, you know, it was a bit of a let’s go AI, big tech AI, all the labs, let’s go, let’s go IPO. Then obviously the deals, some of the deals didn’t come through, some of the IPOs didn’t come through. ⁓ There seems like you even said people are being told to tamper down their excitement a little bit. Is that aligned with what’s happening with the policy side of things? Or is that actually more a reflection of just, frankly, you know, the AI space not being that exciting right now, you know, since the Gentic ⁓ kind of breakthrough. We’ve not seen more consumer and breakthrough. Also, there’s a lot of talk about, you know, there’s no obvious proof ROI on all the spending from all the big tech right now. Help me understand all that, I guess.

Matt Sheehan (38:15)

Sure. Yeah. When I was breaking down those errors, is largely its policy, but it’s already kind of like government attitude towards it. It’s like which, you know, they’re always in some ways swinging back and forth, going back and forth on the seesaw between, you know, control development, control development. And that 2023 to now being one era is sort of in that sense. It’s the period of rebalancing more towards development. There’s tons of sort of wiggles in that process and

things they’re pushing more and retreating on. But from a positive perspective, that’s the overlay. ⁓ In terms of like the capital markets, investments, I mean, I think this is kind of at least for people in the United States, it’s kind of like the one of the most misunderstood things about the Chinese AI ecosystem is that it is really like cash constrained, that it is not like the United States where, you know, open AI is just like sucking in.

the tens of billions of dollars from a huge variety of investors are just spending huge capital outlays, which people talk about, is it a bubble? Is this going to come back to bite them? That’s an open question. But in China, you don’t have the concern about that bubble because there just is not the same level of infusion of cash. when a couple of the companies did IPO recently, Z.ai, formerly Jerpool and Minimax IPO in Hong Kong, and I think I’m not

Grace Shao (39:28)

100%.

Matt Sheehan (39:39)

really an IPO guy. think the IPOs were like modestly successful, but the valuations are just, yeah, the valuations are, yeah, not even close. And ⁓ it reflects a lot of things, but it largely reflects like a funding environment, a business environment, a macro economic environment, and the general sort of attitude towards risk investment. think I was just reading something that ⁓ Ray Ma from ⁓ TechBuzz.

Grace Shao (39:42)

So that’s six to eight billion dollars. The valuation is tiny compared to American peers.

Matt Sheehan (40:06)

China was writing on this. She’s always very good on these topics. yeah, it’s just people kind of assume that there’s like infinite money in China. They’re like, yeah, the government, whenever they want to, they just like turn on the taps and then, you know, it’s like, no, that’s not how it works. And like the VC ecosystem is much smaller, much more new and immature. And so it’s a different story.

Grace Shao (40:28)

on that note you know I was just in SF like last month and I met with quite a lot of investors and people’s kind of I guess misunderstanding was often twofold. One is exactly your point, people are just like oh China’s so rich the government just gives money all these AI companies are backed by the Chinese government I was like 100 % no first of all like there’s some other issues happening in the background but like the government doesn’t even are you know it’s kind of cash constraint and not even that much right now second all these companies are definitely not being backed by the government in any sense in fact

Most of don’t want to take municipal governments or provincial government money because you get kind of tied into, you know, what we’re seeing is, you you get forced into working with the government and it constrains your profitability and commercial goals. On the other hand, another really big misconception was, I thought quite funny was that people often ask, was open-claw frenzy because the Chinese, average Chinese consumer or user were really, really concerned about privacy issues. So they wanted everything on edge.

I was like, hmm, like again, it’s kind of like just not, a major conversation people have. Like I think I hate to generalize, but I think because of how the internet ecosystem is in China, people by default have kind of ceded to not thinking too much about privacy or personal data issues as much. So that definitely isn’t. So I kind of want to bring the conversation that this, you know, like

What are some biggest misconceptions you think people have and how do we help them understand and bridge that gap a little bit better?

Matt Sheehan (41:59)

Yeah, I think yeah some of the stuff that you point out is correct like If you’re if you’re a if you’re a startup if you’re like a small medium company You I’ve talked to these people they’re like actually like we do not want to take government money if we can avoid it not just because we get kind of in mesh but like Entrepreneurs are legitimately afraid that if they take government money and then their company doesn’t work out and they lose the government’s money like they could end up like on the hook like in jail this

legitimate fear that it was stated to me by someone. like, you know, is that happening to entrepreneurs everywhere? No, but it’s like you don’t. ⁓ The government. It does a certain amount of sort of VC-esque investing, but there’s not really that VC mentality of like high risk, high reward. Like we know that most of this is going to go under. It’s kind of local governments at least have been trained on like real estate investment, which is like 10 percent, 10 percent, 10 percent every year.

And this idea that most of these companies that you invest in are going to fail is not really ⁓ deeply embedded there. I do think some of the companies do rely on government funding in different ways. ⁓ mean, Z.ai, Drupal, one of their biggest, maybe their biggest single revenue stream is from ⁓ building custom models, but custom applications for ⁓ state-owned enterprises, local government, stuff like that.

Matt Sheehan (43:28)

When you listen to them in interviews, they’re like, it’s not that big. Maybe it’s 40 % or something like that. But it’s a significant revenue. It’s part of their business model. So there’s that type of a connection to government. With DeepSeek, that’s a company that’s kind of quite mysterious. And we don’t know exactly where all their money comes from. Is it all earned? I think the government got more hands on with them in the sort of aftermath of the DeepSeek moment. You had reports about the government taking passports away from

people who worked there to make sure like you guys stay local ⁓ or the government was like vetting investors was another this is reporting the information. ⁓ But the idea that like these companies are just they just have the kind of like the hose of government money just flowing in at all times and therefore they don’t have to think about anything else is just not it’s just not real. ⁓ They’re they’re much more constrained cash constrained. ⁓

terms of like trying to misconception on Chinese AI regulation, AI policy, this is like my, you know, much of my job is like first getting across like, the trend does actually like seriously regulate the technology. And then, you know, the next layer being like, it’s not all people think, you know, it’s an authoritarian system. She didn’t think he must just kind of like sit down and just like write the regulation. So like nothing matters except what he thinks. And we don’t know what he thinks. It’s like, no, like he doesn’t. There’s this actually very complicated and sophisticated policy ecosystem of,

legal scholars and the companies are doing their lobbying and their thought leadership and, you know, they’re responding to public outcry over things. And I think that’s a, know, you can get this across to people, but it’s certainly not the people’s default mental model of how China works on policy is ⁓ just does not reflect the kind of sophistication in this zone. And it’s somewhat understandable. Like there are policy areas where Xi Jinping just like makes a decision and that’s.

that’s where things are going. Like I think we saw a lot of this in the kind of 2020 to 2022 era. But as an AI policy, COVID, AI policy, it’s not that way. certainly things are not, people are not gonna like, you know, actively push things that are totally against the will of the top leaders, but they are within the constraints of like,

Matt Sheehan (45:52)

the direction of travel, what the CCP is good with, what she wants to do within that kind of very wide lens. It’s really individual people, scholars, bureaucrats, companies that are filling in all the details on this. And it’s a very sophisticated system because they’ve just had a lot of ⁓ had a lot of bites at the apple. They have like passed, I don’t know, eight, nine different A.I. already.

The regulators at the CAC have been getting documentation from AI companies for three, four years. They’ve been building evaluations. been like, and they kind of, got their reps in with AI policy and it leads to a more sophisticated ecosystem.

Grace Shao (46:33)

⁓ yeah, so the last kind of section I want to focus on is just getting into the nitty gritty about, you know, the policy and the security and safety kind of side of things we touched on in the beginning of our conversation. you are one of the few in the West, I think, and talk about the nuance of the word, which you just explained, it’s security, but also safety. ⁓ help us understand.

how to interpret that when we read about that. It actually even helps us understand a little bit of what’s happening in the West. Like, I feel like there’s the governance people, the security people, the safety people, but from someone who might not be in that ecosystem, people are conflating it a little bit. And I just want to understand, you know, how do we understand each of their objectives, again, KPIs, or even their goals?

Matt Sheehan (47:20)

Yeah, yeah, basically, it’s really complicated. It’s very context dependent and it’s always changing. ⁓ I think maybe the first key thing to understand here is like the very particular meaning of AI safety in the West like that. The West AI safety ⁓ largely refers to kind of a specific camp ⁓ of AI development and policy people that are, you know, believe that AI is going to achieve human and superhuman capabilities.

And this could pose like serious, maybe catastrophic risks to people. like, that’s a somewhat coherent community in the United States that has a certain amount of power. Their power kind of ebbs and flows depending on things. But like when you say AI safety in Washington, D.C., it means one quite specific thing. ⁓ In China, that community, it has started to emerge, but it’s much newer. It’s much more recent. It doesn’t have the deep roots that it has in the West.

And ⁓ the way that the word is used in policy documents is both confusing and has changed over time. So a lot of times when ⁓ just to kind of put a little color on the terminology, Anquan, when ⁓ when you’re talking about cybersecurity in Chinese, you say Wang Luo Anquan. So it’s like network Anquan, network security and cybersecurity means something very different from

AI safety from super powerful AI systems posing risks. And so there’s one sort of category of mistakes, which is to be very naive and to read all the Chinese policy documents. And every time they say a word that’s translated as safety to believe, wow, they’re talking about AI safety, they really, really care about this. That is a very naive and incorrect reading of things. ⁓ But in the past, I would say, 18 months, two years,

you have seen a pretty significant uptick in the way that people sort of in and around the system and to a certain extent in and around the companies, their level of attention to what we would call in the West, AI safety to these more kind of large scale, potentially catastrophic risks from powerful AI. I’d say this is, there’s like sort of levels and degrees of this. There’s people talking about this. There’s it showing up in government documents in one way or another.

And then there’s actually implementing this either through like binding regulations or through sort of companies doing their own testing and evaluation to try to their own sort of safety research and their own safety testing. I’d say what we’ve seen so far is a large increase in rhetoric, a large increase in sort of awareness within the policy community about these safety issues. We’ve seen it to start to show up in more significant documents. I think the one you’re referring to early on that I was working working on analyzing is called

They call in Chinese the AI safety and governance framework 2.0, which is in many ways put out by some organizations underneath the CAC, the internet regulator. And it’s kind of ⁓ their attempt to diagram and do an initial discussion of how they see different risks from AI ⁓ and how are they going to mitigate these risks. Oftentimes they’re focused on technical standards as a mitigation. And there was a AI safety governance framework 1.0 in 2010.

fall of 2024 and there was a 2.0 in fall of 2025. And just between those two documents, you can see real increase in the frequency and to a certain extent, the sophistication of the discussion around these risks in China. I’d say it’s pretty significantly below the sort of the AI safety discussion in the United States, but it’s on the radar. will counter, they’re like, okay, that’s great that they’re talking about it, but are what, you

Are they just trying to trick us? Are they trying to make us believe they believe in safety? Are they saying it but not doing it? And ⁓ they’re like, we have not seen much in the way of like, we certainly have not seen like concrete binding regulations that sort of implement safeguards on this front. And in terms of what the companies are doing, it’s quite opaque, but ⁓ we don’t think that they’re doing the, I’d say.

pretty confident they’re not doing nearly the level of sophistication or intensity of safety testing as you see at places like OpenAI and Anthropic. To me, this seems somewhat normal. This is kind of a process. Chinese companies have been behind. The government has perceived itself as being behind. When you’re behind the frontier, you’re not as worried about frontier risks as you’re like other people are going to get to those first and we need to catch up. ⁓ So I see this as kind of like a long-term process. And I think that the sort of increase in discussion about this is

you encouraging if you’re concerned about these issues. But it’s a really don’t want to be just kind of reading the documents and say every time we see Anquan being like, wow, China cares about AI safety. Look at all this stuff. It’s much more ⁓ nuanced and evolving, ⁓ evolving quickly, I would say.

Grace Shao (52:21)

I know like when we spoke a couple months ago, just catching up, you were saying a big part of your job is also trying to help, you know, bring the two sides together. Obviously, you know, ⁓ it’s been challenging given the geopolitical backdrop, but how do you think the global AI governance space can work together? ⁓ Are we going to see, you know, kind of the two world superpowers and two super AI powers, ⁓ you know, guide?

in different directions or do you think there are certain issues where they need to come together and they will come together and are coming together? ⁓ For example, to your point on safety issues around protecting humanity, protecting children, are these things that you are seeing collaboration?

Matt Sheehan (53:08)

Yeah, I’m kind of ⁓ both an optimist and a pessimist on this front in that, like I said, I have very, very low expectations for the United States and China to work together on anything. I have very, very low expectations of any type of a binding agreement or some type of detente where we both shake hands and kumbaya and we’re both going to be very safe with AI and we agree and it’s great. I just don’t expect that. ⁓

So in that way, I’m pessimistic. I think attempts to try to sort of preemptively create these global governance structures that are going to bind both of the countries in advance so we never reach these dangerous thresholds. ⁓ That’s just not where I’m putting my bets. I think it’s good. We need to make all kinds of bets on this front, and it’s good that people are working on this, but that’s not where I’m putting my bets. Where I’m putting my bets is on a much more

limited kind of narrow bore, but I think potentially highly effective form of ⁓ engagement. wouldn’t even say cooperation. I wouldn’t even necessarily say coordination. ⁓ my sort of the term, my mental model for it is something I call AI safety in parallel, which is that like the two ecosystems are going to be moving somewhat in parallel. They’re both going to be pushing the technology forward. They’re both going to be working through safety issues from a technical perspective, from a policy perspective.

And as we kind of move forward in parallel, we’re not going to be telling each other what to do. And we’re not going to be like, okay, I’ll do the safety thing because you are. You told me you’re going to do it, so I’m going to do it. We’re not like sort of moving in lockstep on this, moving in parallel. And we need to have these touch points. We need to have touch points where the two sides develop some form of mutual understanding of what the other side is doing. They understand how other side is thinking about the issues. They understand how they’re perceiving these risks. That’s one of the reasons I do the risk ranking.

stuff and in some cases trying to share best practices, explain kind of explain what we’re doing and why we’re doing it and have the Chinese side explain what they’re doing and why they’re doing it and then where possible share good ideas that we think are sort of uniformly good in the U.S. and China. If we think that we have a policy intervention maybe it’s around ⁓ certain types of pre-deployment testing. ⁓ It’s good to communicate that.

to the Chinese side. And it’s good to have the Chinese side communicate some of ⁓ the reasons and the sort of the specifics of what they’re doing on these fronts. We’re not here to just like trust each other. I think a lot of people are very worried that the Chinese side is going to, is they’re going to trick us. They’re going to say they’re doing it and they’re not, which is like legitimate concern. know, that’s, this is high stakes like geopolitics and powerful technology. So you don’t take anybody’s word for it. But when you have these conversations in talking with people, you can get a,

a sense of their level of sophistication when talking about the issue. If someone is talking about AI safety and they’re like, yes, humanity first, protect the humans, control the machines, that’s our policy. And it’s like, okay, is there anything more to that? They don’t have more than you kind of know that they’re actually not really thinking about it. But if you can get into a more ⁓ deeply engaged discussion, you can see like, actually, yeah, they’re working through these problems themselves. You can see it in the way that they’re.

discussing it. You can see it when they talk about their specific regulatory mechanisms. You can see sort of the connection between sort of action and outcome or thinking and action. And so my model for this is like, we’re not going to agree on things. We’re not going to sort of trust each other. But there are ways that we can both be moving forward at the same time and comparing notes, checking in, getting a sense of what the other side is thinking and doing that I think could contribute to safety in a meaningful way.

Grace Shao (57:02)

I think that’s fair and I think the word you kept on using trust is quite interesting because I feel like whenever I speak to people in the industry, ⁓ there’s just such a lack of trust even within whether you want to say countries or communities or beliefs and value systems and a very, very optimistic, naive way, I really hope that there can be a bit more consensus on certain things like that need to be protected in practice, like such as children, right? And how we go ahead with that. ⁓ But okay, I don’t want to end on a super somber note or anything, but... ⁓ The takeaway is trust nobody. That was...

Matt Sheehan (57:34)

It’s optimistic in a way. think this can’t... When you do see... Well, trust nobody, but

talk and see if you can share some good ideas along the way. think there is real... ⁓ I’ve seen some real sort of traction from these type of things and I think it’s limited. We’re not going to get some kind of hard guarantee that China is going to be perfectly safe or we’re both going to...

Matt Sheehan (58:04)

do the right thing. But within with those low expectations, with those kind of pessimistic expectations, there are there’s progress that can be made.

Grace Shao (58:13)

⁓ I do want to ask one question that’s kind of been happening around right now. That’s been kind of happening like the whole idea of Chinese open models seem to take a little bit of a sidetrack and starting to kind of only release their most frontier related models and close weights. ⁓ Obviously from a very like, you know, capital perspective, where I study it is I feel like it’s a lot of it is because they need to see our eye. They cannot keep doing this because they’re not making money. API sales not enough to, you know, sustain the kind of

long-term ⁓ business as well as research costs. Are you seeing anything from the policy side? Like do you think there’s been a policy shift? That’s also kind of why I asked earlier if this year somehow, know, last year there was a public embrace by the government saying we should open source our technology. Has there been a shift?

Matt Sheehan (59:04)

So ⁓ I’ve seen sort of little tidbits around ⁓ sort of public policy concern about open weight models, but not enough that I would call it a shift. ⁓ In that document, the AI safety governance framework 2.0, it was interesting because it was the first time that there was a fair amount of ink spent on potential risks from open source models. The risks they were primarily talking about were

essentially if there are vulnerabilities in these models in some way, either maliciously inserted or just a vulnerability mistake, those could proliferate throughout the ecosystem because you have all these downstream models and that could lead to impacts. There’s a little bit of a mention of like, maybe open models will be used by criminals and stuff like that. I certainly don’t see this shift in specifically Alibaba strategy as

Matt Sheehan (1:00:04)

in reaction to a significant policy shift. mean, it kind of makes, yeah, corporate decision. It makes if you’re going to be spending tens of billions of dollars building models or at least hundreds of millions, billions, billions of models, giving it away for free is a. That’s a choice. I think there’s reasons why it’s advantageous for China to do that, or at least it was for a stage like it was going to be pretty hard.

Grace Shao (1:00:09)

corporate decision then.

Matt Sheehan (1:00:34)

to get people around the world to kind of believe in ⁓ Chinese models if they were only going to be able to access them through API. ⁓ You know, a lot of American companies that are, you know, deploying ⁓ Quan, Alibaba’s model, I don’t think they’d be doing that if they, I don’t think they would have at least made that leap initially if they had to sign a contract with Alibaba and they believe that maybe their data was going back to China or, you know, the model was more of a black box relative to them. So maybe the open model wave was a very good period of publicity. ⁓

that might pass with time, but I don’t know. I think it’s being more nuanced. I’m not the expert on this. Nathan Lambert, runs the interconnects sub stack, and then Kevin Shue, who runs the interconnected sub stack, are much, much better and more sophisticated on this, and they have good writing on the ecosystem. But from a policy perspective, I haven’t seen a shift. I think it just kind of makes sense.

Grace Shao (1:01:35)

Yeah. Nathan, I actually spoke about this ⁓ a week ago, and I think both of us kind of feel like it’s really more of a capital and business constraint that’s really driving this. ⁓ But I just wanted to hear from you if maybe there was some kind of a top down initiative as to but it doesn’t seem like it. Right. I have one last question for you, which is a question I ask every single guest that comes on. ⁓ What is one differentiated view you hold? And actually, I try not to limit the question to just about AI. However, most people do want to talk about that.

Matt Sheehan (1:02:02)

Mm.⁓ Differentiated view. ⁓I mean, is... I don’t know where most people fall on this, but I think one thing I’ve been thinking about lately is just language learning and what’s going to happen with language learning in the era of simultaneous really good translation either through hardware, devices, or software. And I’ve been reflecting, why have I been spending 16 years or more learning Chinese and just in the...

Matt Sheehan (1:02:40)

mud of trying to learn and remember this language. And I guess my maybe differentiated view on this is I think it still is really important. And I, I waver, you know, I don’t want to just be like, ⁓ you know, justifying to myself why I’ve spent all this time. And like, I, don’t know that I would tell like a 16 year old, like I want you to invest, you know, 10,000 hours on a language when, when you can have it all translated for you, but they’re

there just is something quite meaningfully different about reading these policy documents, about listening to people and hearing the original language and just knowing how that language is used in all these different contexts that gets flattened by translation. I use machine translation all the time. I throw documents into it to get either a first draft or to get something I can share with people. But I guess my view is that I really think

We need people to keep like actually learning Americans to actually keep learning Chinese. And I also just think it’s so much fun. just, was earlier today, I was thinking it was like, why is this, it’s been such a, like a joy in many ways, extremely painful, but kind of a joy to like really struggle with a language over time. And so that’s my take.

Grace Shao (1:03:59)

No, I actually agree with you and I think if anything, I’ve thought about this a lot. So I’m raising a trilingual child by nature because we speak English at home, our parents speak Mandarin, the environment she lives in, they live in speaks Cantonese, right? And I think to your point, in many ways I’m like, wow, it’s actually, it would be so easy for them to travel the world and communicate with people. But the reason why I pushed them to learn the language is really to communicate, to understand a culture and the people and more.

nuanced way, even for myself, like my parents pushed me to learn Mandarin. It like to your point, it’s so painful. But the ability to speak to my mother in her native tongue and understand her is so much more complex and you appreciate much more when you’re older, even though my parents speak English, obviously, but when we were young, we would speak English to him. As I got older, I actually enjoyed speaking to them in Chinese much more because you hear about

Matt Sheehan (1:04:55)

Hmm.

Grace Shao (1:04:57)

It’s also your personality changes, right? Like you kind of get to the core of who they are in their native language and their native way of expressions. So I think for sure, I agree for certain languages, there’s still such value, if anything, even more so to understand a human connection, human connectivity. then for pragmatic reasons, like I took two years of German, I remember nothing. I probably wouldn’t do that again to myself, especially in my class. a bunch of third gen German kids where they spoke the language at home but they can get away with saying they were doing beginner’s German, you know? But yeah, so I appreciate that. Thank you so much Matt, thank you for your time, I really appreciate it, we finally got together to do this episode.

Matt Sheehan (1:05:34)

Yeah, thanks for having me. That was fun.

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Matt Sheehan on China’s AI Policies: Employment, Anxiety, Safety, and State Priorities

AI Proem Podcast

Matt Sheehan on China’s AI Policies: Employment, Anxiety, Safety, and State Priorities