Data has become one of the most important inputs in the modern economy, especially as access to high-quality information increasingly shapes the global race to develop artificial intelligence.
But while US policymakers tend to view data primarily through a national security lens, Beijing is pursuing a much broader strategy aimed at unlocking data’s economic value.
On this week’s Trivium China Podcast, host Andrew Polk is joined by Trivium’s Head of Tech Policy Research Kendra Schaefer to explore why China has formally designated data as a “factor of production” – and how that idea is reshaping the country’s technology and economic policies.
The two discuss:
Why low-cost Chinese open-source AI models are increasingly attractive to Western companies
How restricting access to those models could undermine US competitiveness
What Beijing means when it describes data as the economy’s fifth factor of production
China’s efforts to make data easier to find, price, trade, and use as collateral
Why Beijing views data security rules as necessary guardrails for a functioning data market
How China’s approach could strengthen its AI ecosystem by increasing the supply of high-quality data
Andrew and Kendra also examine the absence of a coherent, pro-growth US data strategy – and why Washington’s overwhelming focus on security risks may be leaving significant economic gains on the table.
Overall, the discussion reveals that China’s seemingly disparate data policies are part of a much larger project: building the infrastructure needed to turn data into a more productive and widely traded economic asset.
Transcript
Andrew Polk: Hi, everybody, and welcome to the latest Trivium China Podcast, a proud member of the Sinica Podcast Network. I’m your host, Trivium Co-Founder, Andrew Polk, and I am joined today once again by a pod favorite, or a pod fan favorite, Trivium’s Head of Tech Policy Research, Kendra Schaefer.
Kendra, how are you doing?
Kendra Schaefer: I’m good. I’m good. How are you?
Andrew: Oh, yeah, I can’t complain. I’m excited for this discussion. Always good to get back in a rhythm with the pod after being off for a couple of weeks. So, I got to talk to Dine last week, get to talk to you this week. So. I’m excited about it. Thanks for coming on.
Kendra: Of course.
Andrew: I am going to talk to Kendra today about some of the research she’s been doing kind of on an ongoing basis for a while now, specifically around how Chinese regulators and Chinese policymakers think about data and how to sort of use data in the economy, how to govern data, all of that stuff. The framework is data as a factor of production. We’ll get into what exactly that means.
So, we’re going to do a deep dive on that. It’ll be wonky, but super unique research that Kendra has been doing that I’m excited to get into. Before we do that, though, we are going to talk a little bit about some of the latest developments in the kind of China tech space around AI, specifically around what’s happening with open-source models and more Western firms opting to use open-source models for cost purposes and potential restrictions coming both from the Chinese and U.S. side on those models.
So, we’ll touch on that briefly before we get into Kendra’s research. But before we do that, of course, we have to start with the customary vibe check. So, Kendra, how’s your vibe today?
Kendra: My vibe is actually really mellow. Nothing catastrophic has happened in the China space in the last 48 hours. And I’m pretty excited. I’m going to Taiwan. I think I mentioned the last time I was on the pod, I had an Asia trip coming up, and now it is imminent. I’m going in a couple of weeks to Taipei with the Brookings Institution delegation. So, I am pumped.
Andrew: Yeah, that’s exciting. Good to get back over to the Asia time zone. I know it’s been a minute since you’ve been over there. It’s always nice to get back on the ground and hear what people are saying. I know that’ll be a great trip. Very cool that Brookings is having you along for that. So excited for you. My vibe, similarly mellow. I feel like we’re sort of in the dog days of summer.
You know, it’s like you said, nothing catastrophic has happened. Our clients, in a good way, seem like they’re not having any fires they need to put out. And so, we don’t have people blowing up our email inboxes first thing in the morning. Oh my gosh, we need to figure this out, figure that out. So, I’m just kind of leaning into the casual summer vibe. So, we’ll bring that mellow vibe to the podcast today.
Kendra: I don’t know if I can promise that based on what we’re going to talk about.
Andrew: Well, I was going to say, Kendra Mello is sort of calm before the storm by definition. So, it actually makes me more nervous when you’re like, “Oh yeah, mellow.” I’m like, uh-oh, something’s coming. But no, we will channel your energy into the discussion today. So, that’d be great.
Kendra: Okay.
Andrew: Of course, before we get into the content, though, we also have to do the quick housekeeping up top. So, a quick reminder, we’re not just a podcast here. Trivium China is a strategic advisory firm that helps businesses and investors navigate the China policy landscape. That, of course, includes domestic policy in China, much of which we’ll talk about around tech and data factors today. But it also includes policy towards China out of Western capitals like D.C., London, Brussels, and others.
So, if you need any help on that front, on any of those fronts, please reach out to us at hq@triviumchina.com. We’d love to have a conversation about how we can support your business or your fund. Or if you just have comments on the pod content, reach out to us. We always love to hear feedback from our listeners. I mean, we prefer positive feedback, but we also will take constructive criticism.
Kendra: We’ll make fun of you in the office.
Andrew: Yeah. Behind your back, and then we’ll respond. No, we don’t do that. We never do that. Secondly, if you’re interested in receiving more Trivium content, check out our website, triviumchina.com, where we have a bunch of subscription products, both free and paid. They’re all sort of focused around Chinese policy intelligence. So, we’ve got a bunch of different options. Kendra’s team produces a daily tech policy update. We’ve got updates on policy impacting markets and impacting sort of the business landscape.
So, check out the site. You’ll definitely find the China policy intel option you need. And then, finally, please do tell your friends and colleagues about Trivium, both about the business and about the podcast. It really helps us grow the company. And I say it every week, but we truly, truly, truly appreciate the word-of-mouth recommendations. They mean a lot to us. And a word-of-mouth recommendation is so much more powerful than someone finding us randomly through a quote in the newspaper or whatever. So, we appreciate folks for spreading the word about Trivium.
While you’re at it, leave us a rating on your favorite podcast platform. That also helps us grow the visibility of the podcast. So, with that out of the way, let’s get into it. You ready, Kendra?
Kendra: I’m ready. Let’s go.
Andrew: Well, so like I said, I think I want to start just with some of the recent developments in the tech space. The number one theme or narrative I’ve kind of been looking at in this space for the past few weeks is companies increasingly thinking about or questioning the cost of AI investment, of building AI processes into their internal systems, partly because everyone thought, oh, well, we’ll be able to replace humans more cheaply with automated systems and AI.
But it turns out that it actually is quite expensive. And a lot of companies are finding out that their investments are actually having lower ROI than investing in humans. I think Alex Karp, the CEO of Palantir, had an interview, I believe it was on TV, where he talked about kind of the weak ROI and how it’s making companies rethink how they are approaching the issue. And his, I think, suggestion was that AI companies rethink their enterprise model.
I don’t know if that will happen. But that, I think, is also related to this idea and increasing reporting that a bunch of Western tech companies and startups in particular, partly because of this cost issue, are really basing much of their tech build out on the open source AI models, because they’re either close to the cutting edge or they’re good enough and miles cheaper that it makes sense from a cost perspective for them to rely on the Chinese models.
So, I just wanted to throw that over to you, Kendra. What do you think is happening here? How do you see the state of play in terms of these cost differentials and the dynamics of more and more Western companies taking a look at potentially employing Chinese models to a greater and greater degree?
Kendra: Well, this is an issue, as you know, that’s near and dear to my heart because I not only run our tech practice, like our tech analysis practice at Trivium, I also sit over our IT department. And of course, we are working with models internally. Have we talked about, you know, model cost on the pod before? Remind me.
Andrew: I don’t think so, actually. Yeah, let’s get into it. I mean, this is another one where it’s wonky and this is pretty inside baseball, but I think what we’re doing is actually quite illustrative of this bigger issue. So yeah, let’s talk about it.
Kendra: I mean, I think what we’re doing is the issue and it is sort of half the issue. So I think many of our listeners probably will have already used an LLM programmatically. They will have tried to interact with an LLM. They are coders themselves or are vibe coding apps and stuff like that. But there’s also a large subsegment of listeners, I think, who probably haven’t done that and don’t really understand what the cost issue is. We have had a sort of intimate experience with understanding where Chinese models are kind of winning the day and where they aren’t.
So, I want to not make that such a squishy conversation, but give a very specific example. So, for illustration’s sake, so we use LLMs for processing massive amounts of policy documents. So, just for illustration’s sake, and this isn’t exactly what we’re doing, but let’s just say we need to take a million policy documents and flag, you know, it would take a human countless hours to read all of those and figure out whether or not they’re related to a specific sector, autos, semiconductors, whatever it is, or if they have a subsidy amount in them and what that subsidy amount is, right?
But we can take that giant pile of documents, and we can pass it through an LLM and ask it to do that analysis and then maybe sell that output to a client or use that output in our research or whatever it is, or create a data product with that output. Processing a bunch of policy documents is a low-stakes, low-security use case. It doesn’t matter if the model is Chinese or just parsing boring open-source documents. There’s no client data going across that channel. There’s nothing, you know, even remotely sensitive that is sort of passing across those queries.
And we’ve tried these processes internally with both U.S. models and with Chinese models. And the bottom line is that the U.S. models are two to 10 times more expensive. And I think for one project that we ran some R&D on, it was like 20 times more expensive. That cost differential decides whether or not our product is profitable. Can we even build this? Should we even do this? That’s a huge difference.
It’s the difference between it costs us $100,000 a year to run this service, or it costs us a million dollars a year to run the service, and clients won’t pay for it. So, it’s really kind of that cost is a real make-or-break thing. There was one tech CEO, I think that was quoted, I think we quoted him in the Daily a couple of weeks ago, I think it was the CEO of Lindy, which is like an office productivity platform who announced on their blog that they’re using Chinese models for some of their features. And he just said, “I don’t need God to write my emails. I don’t need God to write my emails,” which is true for so many use cases, right?
And so that’s not a U.S.-China thing. It’s just a cost thing. There really isn’t a US alternative where the model’s pretty good. It’s good enough to handle those kinds of things. And then, in addition to that, the cost of it is cheap. So, there’s a thousand reasons that a company would choose cost over quality. R&D, you know, you’re just like testing a theory, you’re making a prototype, you don’t want to use the best equipment, you just want your proof of concept so that you can get to a place where maybe you switch to a U.S. model after that when you want a better quality, you know, or you’re kind of looking for top dollar.
Andrew: Yeah, that actually raises a point that I just want to throw in quickly, which is probably, I mean, is an obvious point to everyone like you who’s using these LLMs and to a lot of companies who are trying to figure this out, but maybe not to some people, which is there’s no perfect solution typically with this kind of thing. You’re constantly toggling or adjusting the dials between speed, costs, and quality, right? Quality of output. And so, at various times, you’re optimizing for different ones. Obviously, every company wants the highest quality, the fastest speed at the lowest cost, but sometimes you have to trade off on some of those things, and the Chinese models give you a different trade-off at times.
I guess one other question for you, if you can talk about a little bit is, you know, for what we do in terms of kind of looking at Chinese policy documents and other things in that area, are the Chinese models better with working with Chinese language material, or is that not right?
Kendra: Oh, a thousand percent. I mean, but our use case is so niche, it almost doesn’t matter. Maybe our listeners care. Definitely, the Chinese models are better at Chinese policy documents than the foreign models. But I think for most people, that’s probably not really that big of a consideration. But it’s like, I do think that for most companies, unless you are a coding firm, unless you are a bleeding edge tech firm, there is a lot that companies can do with LLMs.
I mean, and we’ve only started to scratch the surface of adoption, right? Corporate adoption really hasn’t filtered out. And we work with lots of companies who don’t use AI at all yet, right? So, it’s just there’s this huge space where you’re going to have companies who want to use all kinds of models for all kinds of purposes. It’s not like we use four different models in our work, and we just use the right tool for the right job. But if the only tool available is the top-of-the-line, most expensive tool off the top shelf, that is very problematic for our economics.
Andrew: Yeah. We can talk more about this on later pods. I’m sure folks would be interested in how these models inform our work and what some of the behind-the-scenes stuff is. I mean, I think it’s interesting. I think people think it’s interesting. But today, I don’t want to spend too much time because I want to get to the data factors piece. But before we do that, the additional piece of this is since there’s been sort of more reporting about how U.S. companies in particular are using more and more Chinese models, the U.S. government, of course, has taken interest in this issue.
And over the past week or so, there’s been rumors, particularly flying around on X and things like that in the policy space where people are saying the White House, in particular, the U.S. government is considering trying to restrict access to Chinese open source models. And there was a suggestion that an executive order to some effect on this might be coming out, but the White House has denied that. But anyway, I just wanted to get your thoughts on, you know, what you think about that as an issue, you know, whether or not the U.S. government should do that.
I can guess what your answer is to that, but also how that would kind of work and just, I don’t know, provide some context to us about that latest reporting.
Kendra: Well, yeah, I’m sure you can guess how I feel about it. Basically, unless there is a really also not just one good U.S. alternative that is a low cost and good enough alternative, but a robust ecosystem of competitive U.S. alternatives, it is a real bad idea to restrict access to the models that allow innovation to happen in small businesses, in the laboratory, right? All of those kinds of things. There are other reasons besides cost to choose an open source model.
That includes being able to download it and install it on your own machine at home or more likely in your own private corporate data center, which you can’t really do with U.S. models. So, the U.S. just simply doesn’t have a great alternative. And I think you said something to me earlier, which really rang true, which is like if the U.S. decides to try to ban access to Chinese models, and I’ll talk about how I think they might be able to do that in a second, but if they go that route, I mean, it’s basically the same route as saying, “Hey, we can’t manufacture a good NEV either. China’s got cheaper, better NEVs now, but we’re just not going to allow them into the market.”
Did you see the, I think the CEO of Ford a couple of days ago, you know, it was like one of the New York Times headline essentially said, “Look, we support the U.S. in blocking Chinese cars from coming into the market for now, but you absolutely aren’t going to be able to keep them out forever. And we have to be able. in the long term, to compete on a playing field with Chinese manufacturers.” And it’s the same thing here. It’s like, okay, well, you can ring fence the United States for a little while and let everybody else use cheaper open weight models. But the economics get real wonky the longer you hold that line if we don’t have a good alternative and we simply cannot be competitive.
So, I think that has to be addressed. If they want to do a ban, all right. But man, we better have a good alternative and a plan for how we’re going to offer cheap processing to domestic companies or I think it’s stupid.
Andrew: Yeah, well, and I hate the reaction being, the knee-jerk reaction to being we want to win XYZ part of the tech race. And so, we are just going to keep China out of our market. Like, it just strikes me as such sort of simplistic thinking, like we want to win. So, we’ll just kind of block them. And like the way you win is be the most competitive.
Kendra: Right. That’s what I mean. You don’t tie your opponent’s shoes together. That only gets you so far. You know, you might win a couple rounds doing that. But I just don’t think, over the long term, that’s not a sustainable strategy. We can’t just keep saying, “Well, okay, then you just can’t sell. You may not have a better one. You can’t sell that here.” I mean, it just isn’t…
Andrew: Well, and that doesn’t even account for, you know, what does that do for the global landscape? Like, you do reduce your competitiveness globally. And now, oh, great. Well, all U.S. companies run on really expensive U.S. models while the rest of the world works on just as good or nearly as good, very cheap Chinese models. Like, that’s not a positive outcome.
One quick thing before we finally pivot is you also, I said we weren’t going to get into this too much, but you’re unclear exactly whether or not the U.S. government can keep open-source models out of… how do you even enact a ban like that?
Kendra: So, I think from what I understand, there’s a couple of options under discussion. The first one and the most obvious one, although this has already been done to some extent, I think, is federal procurement bans, basically, right? Which is what they did with TikTok is the very first step the federal government took was that you can’t put this on a government device, which is just that’s very low-hanging fruit. But they could also say any government supplier can’t put it on, you know, can’t use it either, or you can’t be a government supplier. So, there’s those kind of that could extend in that way, or you cannot use this tool on a government contract, basically. So, they could go that route.
I think the main concern is that the Commerce Department is going to use the ICTS, like sort of supply chain restrictions toolkit that they’ve got. Basically, the USG has a rule that essentially says if a tech product or service comes from a foreign adversary and could be used to spy on Americans or sort of threaten U.S. national security in some way, then commerce can kind of ban it from the U.S. market or force changes to how that is used.
The problem is that this rule regulates transactions. So, it’s kind of awkward to try to characterize downloading open-source models as a transaction. So, the question is, which touchpoint would they go for? They could maybe go to cloud companies and say, “No U.S. cloud provider can host these models, which is mostly how people are using that.” It’s a large, not everything, but it’s a large chunk of how U.S. companies are using those models. They’re going through Amazon. So, you could do it that way.
They could try to go to like Hugging Face, which is where models are listed, where a lot of these open-weight models are listed and try to ban them from listing it in some fashion, which would make it difficult to download. People wouldn’t know where to go to get it.
Or it would be, I’m sure in two minutes, somebody would put up another website and just like post it.
Andrew: Yeah.
Kendra: So this is difficult to enforce.
Andrew: Our colleagues didn’t think my joke was funny, but obviously it’s just going to be on the dark web, which is where I’m most proficient.
Kendra: It’s where you hang out.
Andrew: Yeah, yeah, exactly.
Kendra: That’s where you hang out all the time. I mean, okay, and so there’s that. And they could also, I think, use the, what is it, the emergency powers, IEEPA, right? They could kind of declare it an emergency and go for it that way. So, there are things that they could essentially do. I very much hope that policymakers are weighing what it would mean for U.S. firms to not have access to that kind of technology. And what I would love to see is if the U.S. government focuses on how to incentivize the development and release of a cheap open-source U.S. model, all this goes away.
I don’t care if I’m using a Chinese model, to be perfectly honest. I’ll deal with like a slightly crappier… you know, if I don’t have to deal with any U.S. government problems, I don’t care if I’m using a Chinese model or U.S. model. I care if it’s cheap and good enough. That’s all I care about, right, as a developer. So, why don’t we just focus on figuring out some policy incentives to make sure we have one of those? I don’t understand why that’s not the primary topic of discussion. Or maybe I’m just not in those rooms, and maybe it is. But anyway, yeah, that’s my thinking on that.
Andrew: Yeah, well, we’ll leave that on the to-do list, figuring out a policy agenda to advance U.S. open source or U.S. developed open source models. I’m sure someone somewhere is having that conversation. But that’s all super helpful, very interesting stuff. We’ll, of course, stay on top of all of that as it develops because it’ll be an important part of not only what we do, but very important for our clients as well. I want to pivot now to your research. We’re going to get into your work on data factors or data as a factor of production.
And this is kind of evolving thinking in the Chinese side around how the government treats data, how everything from taxing data to, you know, data ownership, all that stuff. So, you’ve been doing this research for a long time. How long, you’ve been doing this? What? For like six years now?
Kendra: Yeah, I started in 2020. It’s been six years. I have been cornering people at parties about this and torturing them for six entire years.
Andrew: Well, that sounds like a fun party. Remind me not to go to any of your parties. So, the topic overall is what? How China thinks about data. Is that not something that sort of we already know the answer to? I mean, it seems like it should be relatively straightforward, but maybe I’m wrong.
Kendra: Yeah, no, you’re right. I mean, I think that’s a perfect place to start, because if you ask anybody in D.C., what’s the big U.S.-China data issue or how does China think about data, you’ll probably get something to the effect of China’s primary goal is to steal sensitive data from American citizens or the United States, and the U.S. has to prevent that from happening, right? That’s the vast majority of the D.C. conversation on U.S.-China data.
Andrew: Yeah, that’s a little mind-numbing for sure. I have had that conversation many times in Washington, but what’s the conversation more if you talk to people about this outside of the D.C. bubble? How are people thinking about this that aren’t so focused national security and policy and that kind of thing?
Kendra: I mean, I think the other group of people that we talk to about this is foreign companies that operate in China. They’re not obviously as worried about data exfiltration, but they’ll kind of tell you the biggest issue is cross-border data flow, right? China’s got one of the strictest cross-border data regimes in the world. And for the last five years, I think multinationals kind of been tearing their hair out trying to get their own information out of China. And so that’s basically what corporates are talking about.
So, DC is talking about China’s trying to steal our data. Corporates are talking about how do we get our data out of China and how do we comply with Chinese data laws without screwing up our R&D processes and stuff like that. But as far as I’m concerned, both of those views or both those conversations really only look at a teeny, teeny, teeny, tiny corner of the conversation that is happening inside of China about data. In China, the government has been having a very broad conversation.
They’ve essentially developed a sort of part theory, part national strategy about what role data plays in the economy, how to activate the economic power of data, how to use data to boost GDP and make gains, and how to kind of bolster technological competitiveness by increasing the supply of data. So, we saw this start kind of six years ago, and then we’ve just been watching that theory evolve over time. And it’s now driving this huge wave of Chinese tech policy.
And I think that wave is sort of flying under the radar a bit in the U.S. You don’t often hear people talk about how the Chinese government thinks about data.
Andrew: Why do you think it is so under the radar? I mean, if this is like the fundamental thrust behind the conversation in China, why isn’t it on, you know, more people’s agenda here?
Kendra: Well, that’s a good question. I mean, I think two reasons. One, you know, all of the data policies we’re going to talk about today, individually, if you look at them by themselves, they’re just deeply unsexy. It seems very uninteresting. They’re really interesting in aggregate, but they’re very uninteresting by themselves. And so, unless you can see what they mean in aggregate, looking at one particular piece of it, isn’t that fun?
And then, two, I think the way that China’s looking at this is so different. I mean, deeply different from how the U.S. talks about data that it kind of doesn’t even register. It doesn’t pattern match to anything in the U.S. policy conversations. We don’t see it.
Andrew: Well, that, I mean, I think is exactly why you and I wanted to have this conversation, right, is to start highlighting this. But why do you in particular think it’s so important at this moment that we, yes, the U.S. policy community start to see it for what it is now?
Kendra: I mean, I think the answer is pretty easy, right? Data supply is now a core input to AI development. The AI competition that everyone’s obsessed with is in part a data competition. So what we’re going to talk about today is a very heady idea, right? How the Chinese state views data. What is the long-term strategy? You know, what’s the big idea underneath these little policies, and what that means for the U.S.?
But that’s also now very intimate. Like five years ago when we started looking into this, that was a very squishy concept. But now it has this immediate economic impact because of how important it is or because of how critical and central data is to artificial intelligence.
Andrew: Yeah, good point. All right. Well, let’s get into some of the details here. Where do you want to start in terms of diving in?
Kendra: Okay, awesome. So this is me cornering you at a party now.
Andrew: Oh, no. Look at the time.
Kendra: All right. So this is kind of going to sound like a bait and switch, but I want to start this conversation with a concept that doesn’t seem to have anything to do with data at all, because getting into how China sees data sort of hinges on understanding the sort of econ 101 concept, which is what is a factor of production. And I think a lot of our listeners probably remember this from school, but I don’t know, you’re an economist, do you want to give us the 30-second refresher, remind everyone what is a factor of production?
Andrew: Yeah, I mean, I think I can do it in less than 30 seconds. I mean, traditionally, factors of production are land, labor, and capital, right? So think about the agricultural economy, you’d need land, labor, of course, humans, the people who’d do the work, and then capital being both money and equipment. So equipment, of course, matters in agriculture, but also in manufacturing.
So, basically, the fundamental inputs that you need to produce economic activity is what we think of as factors of production.
Kendra: Right. So, a factor of production is the input necessary for businesses or whoever to create economic value. And if they don’t have those things, they cannot create output. And there are typically, I think in traditional economics, there’s four, you said land, labor, capital, and then China calls the fourth one technology. I think the U.S. calls it entrepreneurship, but basically like IP know-how, you know, like...
Andrew: Yeah. Well, I would call it sort of productivity. Doesn’t matter. I won’t be potentially on that, but it’s really how those things interplay. Like, basically, productivity is how well humans use capital and land.
Kendra: Right, right, right.
Andrew: That’s like a little bit, but anyway, yeah.
Kendra: Right. So, if you’re going to do business, you need somewhere to operate. You need people to do the work. You need money to fund it. You need to know how to put it all together, right? So that idea of those are the inputs to the creation of economic value, that idea has essentially been stable for about a century, right? It’s the sort of periodic table of economics and nobody messes with it.
Andrew: Yes. And I feel like there’s a but coming here in the China context.
Kendra: But in 2020, China did actually mess with that idea. So this is a kind of interesting part. So, in 2020, the State Council released this high-level macroeconomic policy. And buried in that policy was something quite remarkable, right? The policy basically designated data as the fifth factor of production. So now, according to the sort of canon of socialist economic theory that China runs on, and remember, that’s like the foundational theory that the entire state apparatus uses to make policy, right? We’ve decided that this is the sort of economic theory. And based on this theory, we’re going to make some rules and we’re going to make some policy incentives.
There are five factors of production — land, labor, capital, technology or whatever, and data.
Andrew: Mm-hmm. And what’s the point of adding data? I think it’s somewhat obvious based on what we have talked about so far, like pretty obvious input. What do you think the point is of China to elevate data to that level in the canon, so to speak?
Kendra: Well, I think by doing that, what the state is formally saying is in a digitized economy, companies need data to produce economic value, right? As you said, in the agricultural economy, let’s say 300 years ago, if you wanted to create value, you need a plot of land and you need a dude to farm that land. So you need land and labor.
Andrew: Dude.
Kendra: But in the digital economy… a dude, a dude. But now, in the digital economy in the modern age, you need data as an input, or your company needs data as an input in the same way that they need financing. And so that sounds abstract, but it actually has these enormous practical implications because like, think about what that means. It means the state is taking responsibility. If the state names something a factor of production, they’re basically saying the state is responsible for making sure that companies can get this thing.
Andrew: Yeah, that does make sense. And I mean, in a way, with agriculture being such an important part of kind of how Chinese policymakers think of the economy, they would never actually drop land as a factor of production. But you can see for most modern economies, land is sort of less and less an important one. So, it’s almost like you could add data and take away land. Like, for our business, we don’t need land, but we do need data. But that’s just a quick point. But more like, what do you mean like in terms of people or companies getting data? What do you mean by getting it?
Kendra: Well, so, okay. So, it’s the state’s job to create a market environment where businesses can access the inputs they need to grow and contribute to GDP, right? So, if companies need labor, that’s fine. It’s on the state, then to kind of build an education system that produces the right workers or to write employment laws that like balance the needs of employers and employees so that talent can flow smoothly between firms and hiring and firing can happen while balancing everybody’s needs. So, it’s kind of on the state to create the background, the environment in which labor can get to companies, where they can acquire it and use it well.
And then if companies need land to build a factory, it’s kind of the same thing, right? It’s on the state to run zoning, to run deeds and titles, to write property and ownership laws. Those are things that we take completely for granted. It’s like invisible infrastructure of the market. We never even think of it. But those systems are basically what keeps factors of production moving throughout the economy and keeps them flowing into…
Andrew: Companies and enterprises. Yeah, that makes sense. So you’re saying basically that this same logic, at least in the Chinese context, now applies to data. The state is taking a role in making sure there’s an ecosystem that sort of curates and feeds data into companies, broadly speaking. Is that right? Do I have it right or is it different than that?
Kendra: Yeah, yeah, exactly. Exactly. The state’s saying, “Look, there’s already a capital market. There’s already markets for land and natural resources. There’s a labor market. And now it’s on us to build a data market, the systems, the regulations, the standards that basically govern how data gets bought and sold and traded so that it can sort of circulate through the economy and so that businesses can get our hands on it.” And in order to describe that idea, the state has basically formulated or coined this term data factors, meaning data when we view it as a factor of production, data as an economic input.
Andrew: Okay. Yeah, that makes sense. I guess the question then for me is when you talk about “building a data market,” you know, strikes me that data gets bought and sold all the time without the intervention of the state, right? And there are data brokers, there are entire industries already existing around this, both in China and elsewhere. So, why does the state need to build anything? Like, what specifically does it need to build?
Kendra: I mean, actually, that’s such a great question because I think there actually is a big open question about whether or not the state needs to do anything or needs to take an interventionist approach to this at all. But I think if you asked Beijing what the issue was, they’d say that for every other factor of production, humans have been trading it for, in some cases, hundreds of years, right? We’ve been trading land for hundreds of years. And so, the rules of the road are kind of ancient. I mean, we solved the fundamental plumbing problems that make those markets run to the point we don’t even see them anymore.
But none of that plumbing is there for data. Okay, so that all sounds squishy. We’ve been very squishy. Let me get very concrete. Let’s do a concrete example. So, imagine that you wake up today and you decide, I want to buy an acre of forest land in Washington state. So, what is the first thing you do after you’ve decided to do this?
Andrew: Well, either Google or ask an LLM or a ChatGPT, where do I buy land in Washington? I mean, no, I guess you sign on to some third-party site, like a Zillow for land.
Kendra: Right. You would know exactly what to do. You want to buy real estate, you open a real estate website. There’s a real estate market at your fingertips. You would open one of a dozen well-known sites, all of which are kind of pulling from these centralized property listing systems that have been there forever and you just browse what’s available. Consumers know where to shop, no bigs. Now, imagine you want to go buy access to regularly updated shipping container movement data. Now what do you do?
Andrew: Same answer, right? Google, ask ChatGPT. I don’t know. I mean, truly, that’s where I’d start.
Kendra: But there’s not like containerdata.com. Like containerdata.com is not like It’s a common marketplace where all data sales are happening.
Andrew: Website idea.
Kendra: Oh, there we go. We can just quit what we’re doing right now. So, there’s like, the real estate, there are well-worn pathways for discoverable real estate and not so much for other kinds of data, right? You’d like, you’d poke around online, you’d Google it, but there’s no… a business can’t wake up and say, I need this very specific kind of data and I know where to acquire it in most cases. Does the supply of data you want even exist? Who has it, right? And so, the reason data brokers exist is because you go hire these people to find data for you because there is no place that you can just simply go find it yourself in most cases, right? So, that’s one problem, discoverability. How do I discover the supply? Where is it? How do I get it? Does it even exist?
Problem number two, okay, you’re back on Zillow. You’re buying your acre of forest. How do you figure out what you should expect to pay for that data?
Andrew: Compare… well, see what’s out there, right? Look at what’s on the market and compare them to, I guess, decide the parameters of what you want and compare them to other comparable acres of land, houses, etc., whatever you’re trying to buy there.
Kendra: Yes, exactly. You look at comps, or you look at a house with the same… if you’re buying real estate, you look at a house with the same number of bedrooms and bathrooms that you’re looking for in the same street. And you’ll say, “Oh, with the same square footage,” and you’ll say, “oh, it usually sells at this particular price.” You found your million-dollar parcel, right? Whatever.
And then the value, whether or not that value is correct, basically gets confirmed through an appraisal in the process of buying your property. And it’s the same with the labor market. If you want to hire a senior engineer with 10 years of experience, you check Indeed or ZipRecruiter or Glassdoor, and you see what everyone else is paying for the same set of skills. And of course, capital markets have decades of these sort of established valuation methodologies. So, you can find the price for similar items easily, whether you’re buying or selling.
Now, if you’re buying or selling that shipping container data, what should you expect to pay for that? How would you know that you’re paying fair market value if somebody does quote you a cost? And if you are selling data, how do you even know what it’s worth or what you should be charging for it at all?
Andrew: Yeah, I mean, I guess no real answer. I don’t really know. But I mean, fundamentally, I guess it’s worth whatever someone’s willing to pay for it.
Kendra: Right. Yeah. 100%. There’s no real standard metric for valuation. This type of data is valued at this amount of money in general. Right? It’s very hard to do that. A, there are so many different types of data. But B, we just haven’t been selling it that long. And it’s hard to compare one data transaction to another data transaction right now. And so, I mean, I think this is very interesting, but the inability to put a very clear standardized value on data actually creates this sort of cascading set of downstream problems.
And here’s my favorite one. Let’s say you’re a small tech startup. You don’t really own that much physically. You don’t have any equipment, you don’t have real estate, you don’t have tractors or anything. But you’re sitting on a genuinely valuable data set, or you’ve collected or made some data that is worth a lot. You think it’s worth a lot. That data is your most valuable asset. Now you go ask a bank for a loan.
Andrew: And of course, They want like collateral or something to back the loan.
Kendra: Right. They want collateral. You don’t have physical assets. Physical assets work in collateral in part because they’ve got a clear value. The bank knows it can resell your equipment for a million dollars if you default. But if it takes your data, which is your only asset as collateral, what are they going to recoup on that? Where are they even going to put it? How would they offer it to…? They can’t price it. They don’t know what it’s worth.
And so, that creates this situation where data-rich companies that don’t have a lot of assets, which is to say like a lot of tech startups, become a sort of structural advantage when they’re looking for financing. They can’t use this valuable thing that they have.
Andrew: Yeah, I guess I had not thought about it from that aspect in terms of becoming a structural challenge for capital allocation. I mean, I think maybe the U.S. and the West broadly may be a little bit better at that through venture capital, but that’s like, basically, gambling is the wrong word, but you’re taking big bets on something you have no idea about. And China has obviously a venture capital ecosystem, but there’s a long-term problem that small companies, innovative companies can’t get capital. So this makes sense that it would feed into this issue of lending issues, capital allocation issues.
Kendra: Yeah, exactly. I’m going to give one more example just to give a little bit more meat on the bones. So let’s say you bought your land, you have purchased it, and now you go to closing, and it’s time to take ownership of that land, right? There’s a mechanism for doing that that is very well worn. The deed gets transferred into your name, and that transaction and whose name is on the deed gets registered with some kind of county recorder’s office so that forever after, if anybody needs to verify who owns that land right now, they can check the registry.
There’s nothing like that for data. We don’t really even conceive of data as something you would need to register in that way, right? That you would need to kind of confirm that you have the rights to buy and sell and the right to own and the right to use, that there would need to be some kind of allocation. Beijing does think that that is probably necessary. So, you can kind of see these four issues pulling back a little bit, right? All of these things are related to trade, these kind of invisible pieces of it, discoverability, valuation, can you figure out how much it’s worth? Collateralization, can you turn something into an asset that can be used as collateral?
And registering or confirming ownership or rights to ownership over some kind of property. Those are four of the many unglamorous, invisible plumbing problems that have basically been solved for every other factor of production and just don’t exist at all for data.
Andrew: So, you’re saying that basically establishing those four things for data is the underlying project that the Chinese state or policy apparatus is trying to achieve here? Do I have that right?
Kendra: Yeah, that’s the whole project. I mean, not just those four. There’s probably about 20 different unglamorous plumbing problems like that, that the state has identified and gone, OK, we’re going to have to launch a sort of policy initiative to do that. But yeah, I mean, when Chinese policymakers say data is a factor of production, what they’re really committing to is just what we said, define the fundamental rules and processes and systems surrounding transactions so the market can grow.
And the theory of the case is if we make data easy to find, if we make pricing standard and predictable, if we let companies legally sort of establish and protect their rights to data so that they can trade it, then more companies will want to sell data. More companies will buy data. That means more companies will acquire and use data, empowering the data economy and share data and trade data. And so, supply goes up, and circulation goes up. That’s generally, that’s the fundamental data theory.
Andrew: Okay. Yeah, makes sense. All right. So, thanks for laying that out. I think that kind of sets the sort of theoretical and sort of contextual piece of this. But let’s kind of go a layer down. What can you talk about, like an actual policy here, something sort of more concrete that solves one of these problems that the Chinese policy apparatus is putting forth?
Kendra: Yeah, actually, I’ll give you three. I’ll talk a little bit about how the state is actually trying to solve those three problems, like those couple of the problems we just talked about. So, first, the registration and ownership problem, right? How do you confirm you have the right to sort of use a specific data set in a specific way? What we’re seeing now is that the NDRC, China’s big sort of macroeconomic agency, is piloting what they’re calling a data property registration system.
So, you can think of that, well, the way they’ve described it is a land registry or like a patent office, a securities depository, but for data, where data owners and users can register their claims, log rights to use, and then trace the history of ownership of specific types of data. So, in other words, I could basically say I made this data set. I’m putting it on this registry. I think they’re talking about the underlayer maybe being built on blockchain or something like that.
But I’ve got this registered that I’m the owner of this data set. And then let’s say I’m transferring… It’s not really actually with data about transferring ownership. It’s, I’m going to allow you to use my data set for the following purposes. And the right to use the data in that way is then logged in this registry. And the end user can then take that data and use it without worrying that there’s going to be some kind of… you know, there’s like a clear transaction that they can point to and a clear rights document that they can point to that is sort of part of a sort of central depository.
So, that’s the general idea with that. And they’re already kind of trialing that at the local level. Shenzhen in particular is actually running a trial that’s supposed to go national in a couple of months. And last year, we actually saw the NDRC’s National Data Administration put out this call for research proposals on how to construct, basically asking researchers for ideas on how the base construction of that system should be run nationally. So, we see a lot of movement, right? Early movement on constructing a system like that, meaning that companies in China in five years, three years that acquire data, that sell data, that use data, that leverage data in any way, will probably have to transact with this registry.
Andrew: So, this is like the county recorder office registration system, but for data sets, you’re saying?
Kendra: Yeah, exactly. That’s exactly right. So, the second issue, right, discoverability, the where do I even shop problem? This one’s pretty simple. We’ve been watching this for many years. There’s been this sort of wave after wave of state-backed data trading platforms established. They call them data exchanges. Usually, it’s a local government that stands one up. It’s basically a platform where you can browse available data sets.
Most of the companies listing data sets on there are state-owned companies, indicating that the private market is not really that interested in transacting on these state exchanges. So, I don’t know that they’re the best idea, you know, but the state has been essentially doing that. There’s one in Shanghai, there’s one in Beijing, there’s one in Shenzhen, there’s one in I think Guiyang still, where it’s essentially just a centralized marketplace where people can go and kind of shop for the data that they need, or at least that’s the fundamental idea.
Andrew: Okay, got that. But I guess a follow-up question would be, what are they doing, that sort of resource allocation issue that we talked about before, or how to get a bank loan based on your data assets? How are they looking to solve that issue?
Kendra: Oh, well, this one’s actually my favorite because it’s really concrete. State banks are running pilots that let companies use their data as loan collateral. And so, we’ve studied quite closely the structure of those pilots because I think they’re pretty interesting. It’s a three-party structure. So you have the bank that’s making the loan. You have a data-heavy and asset-light company that wants a loan. And then the third party is usually one of those state-backed data trading institutions, so like a data exchange, that independently certifies the value of the company’s data assets, like an appraiser or a data appraiser.
And so, then the bank sets the loan rates based on the value of the company’s data assets. So, there’s like one example, I think from August 2024, when the Chongqing branch of Huaxia Bank partnered with this data trading platform locally and offered a 1.3 million renminbi, so not a big loan, to a company in Chongqing that was doing smart city development. And so then the trading institutions certified the data’s value, the bank priced the loan’s interest rate off the certified value. And that’s how the money was issued. So, these aren’t big numbers. 1.3 million renminbi is not like a massive loan or anything like that. But it’s interesting just to watch them kind of see, does this work? Can we proceed here? Yeah.
Andrew: I mean, that strikes me that that whole system depends on the bank or someone else, some third party, whatever it is, being able to credibly say what the data is actually worth, right?
Kendra: Yeah, yeah, exactly. So there’s another piece, right? Another piece of unglamorous policy plumbing. So, the Ministry of Finance has basically been supporting research into standardized data valuation methods. And we saw a couple years ago in like 2023, there’s this body called the China Appraisal Society, which is like an industry association tied to the Ministry of Finance.
They usually just do physical asset appraisals. And so now they’ve been publishing guidance on conducting data asset appraisals, right? And so, they ask people to look at basically creating a sort of framework for determining for how an appraiser should be able to set a price on data. It’s very interesting stuff.
Andrew: Okay, let me step back for a second. So that all makes sense in terms of domestic flow of data, right? Kind of trying to boost the infrastructure behind the pricing of data, how data can be used as collateral, where and how you can sell it, where and how you can exercise the rights to data. But, I mean, as we talked about before, foreign companies who we work with, non-Chinese companies, are primarily interested in cross-border data, right?
Getting their data, in particular, out of China. And China’s regulatory regime on that front is incredibly strict. So, we’ve worked with these companies trying to get their data out of China for months and months and months. So, it strikes me as actually quite normal for China. But talk to us about that dichotomy where, yeah, we want stuff flowing freely internally, but we don’t want it to go across the border. What’s going on with that?
Kendra: Well, so I’m glad you brought that up, right? Because actually, I think this is the single biggest miscalculation in how D.C. reads China’s data security regime. I mean, the D.C. read is China’s data security rules are digital protectionism and that’s it. China wants to build a wall to hoard data inside of China’s borders while they steal data from everybody else’s. That’s kind of the standard, right?. That’s kind of the standard framing. But from Beijing’s perspective, the data security regime isn’t a wall around the market. It’s actually the guardrails that make the market possible.
Like, it’s not unusual for markets to have guardrails, even really, really heavy handed guardrails for cross-border trade. Capital markets have a zillion guardrails for cross-border trade. Labor markets have a zillion guardrails. I’m not necessarily cross-border, but there’s some. And so, the logic runs if the state clearly establishes what kind of trading is not allowed and where the safety risks are, and a lot of those risks are bigger in cross-border trade, and if it clearly defines which categories of data cannot be traded and starts there, then everything outside of those lines can sort of flow more freely and with more confidence.
I urge listeners, anybody who cares enough to, after you finish this episode, go read the actual text of China’s data security law. Go read it. I think DigiChina has a really good English translation. And I promise you it will read differently than you remember if you’ve read it before. There’s all this language in there about how data security is the fundamental building block of data trade, and that security has to be strong before data trading can occur. And that’s how all these data security rules are about enabling the safe trade of data, and the state’s job is to enable the safe trade of data.
And I think we just kind of gloss over that because we don’t, again, it’s not really on our radar that this is the plan. I do actually want to say one other thing, though. So that’s the plan. But China’s data security regime is still over-calibrated. I think they do want trade, but they have significantly overshot on the let’s secure this before we allow trade to the point where the current regime is not serving its own goals. There’s like a genuine desire to enable safe data flows, but the state is kind of its own worst enemy with this like, knee-jerk over-securitization. And so, what we’re watching right now is the state kind of actively hunt for a balance point.
How do we balance development and security? We heard that a thousand times, right? And we’ve watched the pendulum swing really hard towards security. We’ve watched it swing back a couple of times. It’s a live negotiation.
Andrew: Yeah. I mean, that’s not shocking, right? Like the security versus development debate, to the extent that it’s even a debate or finding that balance is an ongoing endeavor among Chinese policymakers, like in a range of areas, right? Data, technology, supply chains, you name it. They’re always trying to strike that balance. So, that’s not shocking to me. And it’s also not shocking to me that they’ve leaned a little bit further into the security side than the development side, which also is normal for governments everywhere, but also in particular for China.
But I think you’ve done a really good job here of laying out kind of the main rationale that China is using to put forth this data governance regime. Some of the specifics around the very concrete plumbing and flowing issues or flow issues that Beijing’s trying to solve. But flip that around. What do we as people who are in the policy community in the U.S. to make of that, what should Western policymakers or policy thinkers take away from this discussion?
Kendra: Yeah, I mean, I definitely don’t think that the United States needs to adopt the idea that data is a factor of production and rush into Beijing’s footsteps and do exactly as they have been doing. That’s definitely not the point. I think the biggest takeaway is that like when you lay China’s approach to data policy next to America’s approach to data policy, on our side, there’s this kind of massive gaping hole where a proactive pro-growth U.S. style data strategy ought to be. China has a pro-growth strategy, so we need a pro-growth strategy.
Every major U.S. ally has already done this. We are the outlier, right? The UK, Japan, the EU, Canada, Australia, all of them have looked at this issue. How can we use data to foster growth? What are the problems we need to solve? What are the pathways we need to take? What are the incentives we need to put in place? And we simply have not done that. And I think it’s because, as I mentioned earlier, when the U.S. talks about data, it’s almost exclusively as a security issue. And when security is all we talk about, then security is all we do.
I mean, just look at the last five years. We’ve done a ton on security. We have secured telecom equipment, smart car software, port cranes, cellular modules. There was the TikTok fiasco. We went after WeChat. We’re doing ICVs, preventing Chinese cars from coming into the U.S. because they collect data on this. So all of those actions was fundamentally about preventing the exfiltration of sensitive American data. And that’s just the entire American policy portfolio right now.
Andrew: Yeah. Okay. So I understand that. I guess the question then to me, actually, I was thinking of this, as you were talking through the Chinese side, is it that the U.S. has just decided like we don’t need a growth strategy for data per se? Or is it like, does the government need to be involved to the extent that China is involving itself here, meaning like our U.S. policymakers just saying like the market will figure this out or which I think it would be…? You know, that might be also an appropriate way to go. I don’t know. How is that conversation happening in the States? Is it just like a growth strategy is nice to have? Should it be left to the market? And where do you land on all of that?
Kendra: I think every time I have heard policymakers talk about this kind of sort of pro-growth strategy in the U.S., it has been talked about like, like those are the Montessori kids. Like that is a kumbaya, get out the guitars and sing together. Let’s all talk about data sharing. Let’s all talk about… like almost it’s taken on this like hard left kind of, I don’t know, let’s all hold hands and share data kind of initiative, right? It’s just got this very strange overlay in the U.S. that I haven’t really seen it take on anywhere else. I’m exaggerating.
There are certain initiatives that have made some progress. But there’s been a lot of that. I mean, there was some government data sharing initiatives where the U.S. decided to try to push more government agencies, is another thing China’s doing, to release more of their data in a format that researchers could use to the general public. And that was treated as this like… you know, there’s some open data laws about what research was supposed to do. Get government departments to share more data with each other so that they could be more, you know, efficient and improve bureaucratic efficiency, all this kind of stuff. But these don’t have any staying power. They die. They go to the back burner.
They get treated as not important. I think because, my personal take on that is that in order to see the value of initiatives like this, you’re looking at a 20-year investment. You’re looking at a 20-year investment in research. You’re looking at a 20-year investment in changing the way that the bureaucracy functions, you’re looking at a 20-year investment before you see any returns. And in a four year or an eight year administration. We’re not good at that, we’re not good at making… I mean, that’s one of the US’s weak points unfortunately. We’re just not great at making investments that we hope will, you know, prioritizing investments that we’re going to reap the dividends in two decades.
We’re great at let’s reap the dividends next year, but we’re just not really good at those kind of long-term goals. And so, I think that’s why, security strategy, you can implement within the span of a single administration. You can ban TikTok in two years. Or I guess not. I guess you can’t. You can try to ban TikTok in two years.
Andrew: Yeah. That took three administrations, technically.
Kendra: That was a bad idea. You can institute semiconductor export controls or whatever. You know, you can put out an executive order in a minute. Generating more efficiency and growth economically from data is like a little bit of a squishy idea and it’s a little bit of, it’s too long-term. And actually, I just want to say, this is real money. This isn’t just a sort of wishy-washy, oh, gross. But there are actually numbers there, right?
The OECD kind of concluded back in 2019 that data access and sharing, if you increase the supply of data in the economy, that it can generate benefits worth 1.5% of GDP if you’re just talking about public sector data. In other words, if you just make governments release more data, then you can really generate a bunch of significant economic benefit out of that because companies will jump on that data and they’ll make new businesses out of it. There’s more data available, let’s make an app that like uses that data to do something, and then that creates jobs and then that creates productivity.
And then if you also account for private sector data, if you basically get companies moving their data around between market actors more than they do, instead of sitting on it or hoarding it or being afraid to share it or can’t be bothered to sell it or whatever it is, then, you know, the range gets a lot bigger. You can get a bump of like between 1% and 4% of GDP. So, it’s like really leaving, actually leaving potential gains on the table in a way that’s pretty detrimental, I think.
Andrew: Yeah. Can you talk actually just a little bit more about the channels through which you see and, again, Chinese policymakers or others, non-Chinese policymakers see like what avenues are there for data to be a growth driver, generally speaking? I think that’d be interesting for listeners as well.
Kendra: Yeah, I’ll give a couple more examples. So, I just kind of mentioned one of them, which is job creation, right? I mean, I think some of the studies that are coming out now are basically showing, as I just said, data is available to startups, to innovators, to entrepreneurs. They come up with cool ideas for creating businesses with the data. If the data is not available, then they don’t do that, right? And that’s especially interesting because you have a lot of situations where the government or a large company or a collective of companies is the only body capable of putting that data together.
I’ve got one example I cite a lot, which is, so in 2017, Deloitte did a cool study. They looked at what happened when Transport for London released real-time transit data through APIs. And I think it was free. If I recall correctly, I don’t remember exactly, but I don’t think they charged for it. But like Transport for London put this out. 600 apps got built off the back of that data. 500 jobs were produced. And the economic savings for the city were like 130 million pounds. That was one data set.
This is one data set on the market. And if you aggregate that across the entire economy, what you could do with that is like pretty cool. There’s some early research indicating that you will get a small productivity boost when firms invest in collecting and using their own data. So, if you basically encourage a company to go acquire data and then transform that data and make, I mean, we’re seeing that in our company right now. We’re using data more than we did before. And there’s a lot more, like we’re doing bigger things faster, right?
We can see it kind of in the way that we’re working at the moment. So, the productivity is a way that you can kind of get growth out of that. And then third, it’s like the government itself kind of gets better. The bureaucracy gets more responsive. People get better public services, right? And China’s a really good example here too. Nobody really liked how China responded to COVID, but they responded really fast. And that, you know, epidemic control was all totally data-driven, built on 20 years of investment in data sets for public health, for transportation, that they just leveraged the minute this disease kind of appeared.
They took all these existing data sets and they pulled them, and started drawing insights on disease spread. And that’s kind of how they did the entire epidemic control measures. And they did that in just a couple of weeks because they’d made that investment already. Right? And finally, now it’s, of course, it’s talking about this a bit, but it’s AI. The big issue in AI is like AI researchers and small AI startups, like specialized AI startups and niche industries really need a steady supply of this high quality data, especially data that’s hard to get.
So, that would be things like, imagine what you could do if you had an entire data set of all of the mechanical equipment failures in smart factories across manufacturers, not just one manufacturer’s data, but every manufacturer’s data. Could you improve uptime, productivity, production speed of machinery? You know, what insights could you gain from that? So, tons of things like that across in almost every sector. And so, you know, health care, another great example. Hard to get good health care data because of various privacy restrictions, etc. But you get tons of benefit from that.
You can cure diseases with that kind of stuff. And so China’s made that producing that supply, this is where we come back to factors of production.
If data is a factor of production, then making sure that supply exists so that these things can happen, it’s a state’s job now. It’s a state’s priority. They’ve taken on that responsibility. They’ve decided to move that ball forward. Right? So anyway, that’s the game.
Andrew: China is obviously pursuing that. And you would say that U.S. policymakers are just kind of leaving that on the table in terms of not having a national strategy for data development and supply.
Kendra: There was a couple of mentions of data in the Trump administration’s America’s AI Action Plan. And when I read those, I got real excited about them. Some of those are really good, right? They’re actually really good ideas. And they have not at all been prioritized as much as all of the securitization stuff in that plan, right? The funding has not gone to those initiatives yet. Tick tock, tick tock. It’s that kind of stuff. It’s like somebody will recognize that, yes, mostly those initiatives were about funding consortiums that pool sort of high-quality data and compute for leading-edge researchers so that researchers were solving that access to research data problem for AI specialists and stuff.
So, it’s not that somebody hasn’t written it down. It’s not that somebody hasn’t said, hey, we ought to do this. It’s that when you look at where policymakers’ time and energy and attention is going, that’s not what anyone’s talking about. When you walk into a room where they’re talking about AI and DC, nobody’s sitting around saying, “How can we really squeeze economic value out of data? What proactive, positive, long-term roads can we lay down so that we really get benefit from data?” That’s not the conversation that’s happening. So, it’s not that it’s not recognized. It’s just not prioritized.
Andrew: Yeah. Well, and again, I just sort of anticipate listeners saying, you know, “Well, that’s not the state’s job.” And I guess my thought would be, of course, the U.S. is never going to take the same state-heavy interventionist approach that China is.
Kendra: Totally.
Andrew: But that doesn’t mean there’s no role for the government to help kind of build this ecosystem. I mean, of course, like as you talked about, the government, whether it’s city government, county government, national government, has taken a role in governing and overseeing transactions and putting guardrails around all the other factors of production, but we just don’t seem… I mean, you know, we haven’t caught up in terms of kind of treating data fundamentally as so structurally important to the economy. I mean, you know, the old, obviously, cliche is data is the new oil, but we’re certainly not acting like it, right?
Kendra: Yeah, yeah, exactly. Exactly.
Andrew: Well, this has been super, super interesting. Obviously, a ton of work that you’ve done on this. And just in case it’s not clear, the work that Kendra has done on this, in case it’s not clear to listeners, was specifically with an eye towards informing U.S. policy. So, everything we do at Trivium is kind of trying to understand China, but this was like an effort to understand what China’s doing in order to kind of make strategic recommendations on how the US might want to be thinking about these issues. And so that’s one of the reasons that we kind of leaned so heavily in the last part of the conversation on what the U.S. is not doing here. I think this is great.
I hope that this work gets some uptake from policymakers and people in that space. We will keep sounding the drum or pounding the drum, sounding the alarm. I don’t know.
Kendra: Sounding the gong.
Andrew: Yeah. And yeah, well, I’m sure there will be a lot more opportunities to talk about these kinds of things. It’s always good to kind of take a step back and do kind of a wonkier, a higher-level… wonky higher level; those are maybe at odds. Anyway, I’m rambling now. But this was amazing. We’ll just leave it at that. Thank you, Kendra, for the time and for walking us through that. I found it super helpful and fascinating. I’m sure our listeners did as well.
Kendra: Awesome. Well, always good to be here.
Andrew: All right. Well, thanks so much. And thanks, everybody, for listening. We’ll see you next time. Bye, everybody.
