Sovereign AI: Why Nations Are Building Their Own Models

0:00They're not being called AI data centers. They're being called AI factories. Industrial revolution having oil was important. They're now having data centers is important. These models aren't just compute infrastructure, they're cultural infrastructure. It's not just self -defining the culture, but self -controlling information space. So if a model is trained by a country that's adversarial to you, that's actually very hard to e -val or e -benchmark when the models are released. This is a massive vulnerability. Is that the new age of LLM diplomacy that we're entering here? Do we build? Do we partner?

0:36What do we do? Today we're diving into a conversation that's just as much about geopolitics as it is about technology. This week, the Kingdom of Saudi Arabia announced plans to build its own AI hyper -scaler called Humane. They're not calling it a cloud provider. They're calling it AI factory, and that language alone suggests a shift. For decades, cloud infrastructure has been concentrated in two places, the US and China. But with the rise of AI, that model is breaking down. Nations no longer want to outsource their most strategic compute. They are building sovereign AI infrastructure, factories for cultural and computational independence.

1:16To unpack what this means for the global AI stack, national sovereignty, and the new digital power dynamics, I'm joined by Anjane Mita in Guido, Apanseller. We talk about what it takes to become an AI hypercenter, why governments are spending billions to control inference pipelines, and whether we're entering a new Marshall Plan moment for AI. Let's get into it. As a reminder, the content here is for informational purposes only. Should not be taken as legal business, tax, or investment advice, or be used to evaluate any investment or security and is not directed at any investors or potential investors in any A16z fund.

1:56Please note that A16z and its affiliates may also maintain investments in the company's discussed in this podcast. For more details, including a link to our investments, please see A16z .com forward slash disclosures.

2:12Onge Guido, we want to talk about sovereign AI, AI and geopolitics, and let's start with the news. Our partner Ben is in the Middle East right now to participate in his own way. What happened and why is his own point? What happened is the kingdom announced that they're going to build their own local hyper -scaler or AI platform called Humane. And I think what it's notable is that as opposed to the status quo of the cloud era, they're viewing the AI era as one where they'd like the vast majority of AI workloads to run locally. If you think about the last 20 years, the way the cloud evolved was that the vast majority of cloud infrastructure basically existed in two places, China and the US.

2:57and the US -endera being the home for the vast majority of cloud providers to the rest of the world. That doesn't seem to be the way AI is playing out. We have a number of frontier nations who are basically raising their hands and saying, we'd like infrastructure independence. The idea of being that we'd like our own infrastructure that runs our own models that can decide where we have the autonomy to build the future of AI independent of any other nation, which is quite a big shift. And I think the headline numbers are somewhere in the range of a hundred to 250 billion worth of cluster build out that they've announced, of which about 500 megawatt seems to be the atomic unit of these clusters that they're building.

3:44So a number of countries with the kingdom being the one that's most recent, I've been announcing what we could think of as sovereign AI clusters, and that's a pretty dramatic shift from the pre -AI era. I don't know if you'd agree with that. I think it's spot on. I think many sort of geopolitical regions are reflecting back what happened in previous big tech cycles, and wherever the technology is built and whoever controls the underlying assets has a tremendous amount of power of shaping regulation, shaping how this technology was being used and also puts themselves in a position then for the next way that it comes out of that.

4:18And, you know, was the Industrial Revolution having oil was important and now having data centers is important. And so I think it's a very exciting development. Yeah, in fact, you can often tell why something is important to somebody by the semantics that folks used to communicate a new infrastructure project. Right. In this case, if you look at how the cluster buildouts are being referenced, they're being called AI factories. They're not being called AI data centers. They're being called AI factories. And I think there's two ways to respond to that. One train of thought would be, hey, that's just branding.

4:54That's just, you know, the marketing people doing their thing. And under the hood, this is really just data centers with slightly different components. But everybody in the world, in every industry, is looking for a way to be relevant in the age of AI, and this is the computer infrastructure world's way of doing that. And a posing view would be, actually, no, this is not just marketing. If you look onto the hood, and if you x -ray the data center itself, very little of it is the same as was the case 20 years ago. The big difference in active components being GPUs, right? About 20 years ago, what would you say the average number of GPUs were in a...

5:29What percentage of... Right, pretty much. Right. It's a very recent phenomenon. And today, I think if you look at the average 500 megawatt data center, and you looked at what percentage of the gap X that was required to build that data center or operated rather went to GPUs. Massive. Yeah. That's a huge shift. I think we're also seeing a specialization. The kind of data center you build for, classic CPU, centric workload, and what you build for high -density AI data center. Look very different. You need good cooling to the rack. You need very different energy supply. I was close to power plant, you want to lock in that energy supply early on.

6:04And then we also see that change, I think in the consumer behavior, where classically you want a very full stack that has lots of services that help enterprise build all these things. We're seeing more and more enterprise that are actually comfortable with just building a top of a simple Kubernetes abstraction or something. And basically, you know, cherry pick a couple of snowflake or database type services on the side that help them complement that. So I think it was a new world. And so that's certainly true that you could kind of look at the technical components in an AI factory are completely different from a traditional data center.

6:36And then there's what does it do? And historically a lot of the workloads that traditional data centers were doing, we're running one cloud hosted workloads for enterprises or developers, whoever it might be, where most of that, the data sets and the workloads were actually not particularly opinionated. And when I say opinionated, I mean they're not necessarily subject to a ton of cultural oversight. You could argue that was not the case with China, right? We're China wanted full sort of oversight over those workloads. Right. But for the better part of the 2000s, until the rise of GDPR, CCP, and so on, we lived in an era of centralization, we're having most of your cloud infrastructure in Northern Virginia, was preferable for most of the world's developers enterprises because it gave them economies of scale.

7:28That started to change, of course, with GDPR, CCPA, the rise of data privacy laws, because then you had region by region compliance. And that made the rise of something like cloud flare critical, right, where cloud flare has this idea of distributed infrastructure where you can die the workload policies to wherever the user is. But by And large, that was critical for especially for the rise of social media workloads. But the vast majority of enterprise workloads didn't need decentralized serving. What's different about AI seems to be that these models aren't just compute infrastructure. They're cultural infrastructure.

8:03They're trained on data that has a ton of embedded values and cultural norms in them. And then more importantly, that's the training step. And then when you have inference, which is when the models are running, You have all these both training steps you add that steer the models to say something or not to refuse the user or not. And that last mile is where things over the last, I would say, year. They have made it more and more clear that countries want the ability to control what the factories produce or not within their jurisdiction. Whereas that urgency didn't quite exist as much. Because of the cultural factors or because of certain independence or resilience or...

8:40It's a good question. My sense is there's two things going on, but you should chime in if you think I'm being complete. One, I think, would be the rise of the capabilities in these models being now well beyond what we'd consider early toy stage of technology. I think our partner, Chris Dixon, has a great line, which is that many of the world's most important technologies start out looking like toys. Four years ago, when the scaling loss paper was published, in GPT -3 was published, most people looked at it and I said, okay, that's cool. It's short, it can produce the next word. It's a nice party trick.

9:13It's a nice party trick, right? It's a stochastic parrot. And now you have foundation models literally running in defense, in healthcare, in financial services industries. TrashyPD has about 500 million monthly active users making real decisions in their daily lives. I think using the ZI model, there was a paper that was recently published by Google that showed the efficacy of Gemini, their foundation model, at solving medical questions. And one of the most interesting things you can see when you look at the usage, the types of prompts that people are using models for, relative to two years ago, three years ago, where it was a lot of helping to write my essay.

9:54It's turned into coding and helped me solve a whole host of medical problems or personal life -related questions and so on, where it's clear now that these capabilities can be used, want to drive mission critical industries like defense healthcare and so on, and also then influence a number of your citizens' lives. And so I think that makes a lot of governments go wait a minute. If we are dependent on some other country for the underlying technology that our military, our defense, our healthcare, our financial services, and our daily citizens' lives are driven on, that seems like a critical point of failure in our sovereignty.

10:33So that's one. It's just that models have gotten good and they seem to be good at a bunch of important things. The second is, I think, an increasing belief that if you don't have control over the models production pipeline, then you're doomed or destined to use models that reflect other people's cultural values. We had a pretty in -depth debate about this with DeepSeek, where the question was, is deep -seek, fundamentally more biased or not, than open source model strained in the US. And I think there's early evidence to say that you can actually see certainly in the post -strained deep -seek that there's just a number of topics and types of tasks that it's been told to avoid and answer differently from a model like Lama.

11:18So that's the cultural piece. I think there's the critical sort of national capability piece and then there's the cultural piece. And I think both are combining to create this sort of huge rise in demand for, you could call it sovereign AI, which is the idea that you want control over what the models can't do or you could call it infrastructure and depends. I think everyone's got a different word for it. You could call it the, our local AI factory ecosystem, but I think that all these terms are trying to get at the same thing, which is we've got to control our own stack. Yeah. I think I would make it even stronger.

11:45I think it's not just self -defining the culture, but self -controlling the information space, exactly. I mean, today we're starting to see how, in many cases, models are replacing search. It might have no longer go to Google, it will go to chat GPT and that comes back with an answer. If there's a historical fact and say in the Chinese models is not show up and the US model it does show up, that is the reality that people grow up with. And if you write an essay in school, in the future many of us as essays will be graded by an LLM. So in fact, in school, something that may be truthful, right, maybe graded as wrong because we'll ever control the model decided that should not be part of the trading course.

12:25It has a very profound effect on the public opinion and so on, on values. Right. The downstream use is an interesting one because it's very hard to measure for. And certainly relative to two years ago when the vast majority of products and applications, like the ones Geeter's talking about, were basically pretty simple models, right? Well, at the time they were considered pretty complex, but the to your changes so fast. Today we'd look back at a model like GPT -4 that was largely just a next word prediction model and say that's pretty rudimentary. Because if you x -rayed an app like chat GPT, sure on the surface it looks like nothing much has changed, right?

12:59It's still a chat box you type in. What you need and it outputs an answer relative two years ago, but under the hood, there's been this insane evolution where there's four or five different systems interacting with each other. Right? You've got a reasoning model that can produce a chain of thought to think through what it should do next, including then doing what we call tool usage, calling out to third party tools. And then you have the idea that these models can start to self -learn, go through a loop of taking your input and reasoning about what it needs to do, calling an action, and then evaluating its output, and then updating that loop.

13:35That starts to look more people use the word agent to call it that. But the idea is that it's going from being a pretty simple model to being a system. and it's very hard to measure where the adversarial cracks are in this system. So if a model is trained by a country that's adversarial to you, to when you're writing code, open up a port or what we'd call a call home attack, where it's transmitting telemetry, that's actually very hard to eval or eobenschmark when the models are released, because these models are often tested in very academic or static settings. And so when DeepSea came out, it was just such a great model.

14:15It was a phenomenal piece of engineering that suddenly everybody was using it everywhere. And a number of CIOs and CTOs got pretty nervous because they were like, wait a minute. If the model is being used in this agentic fashion and I don't have visibility on what it's doing, adversarily until it's too late, this is a massive vulnerability. And so I think the adversarial threat as the systems go from being models to agents is He's causing a lot of governments to go, well, we'd rather have the whole thing running locally in a way that we can lock down. Again, it comes back to a sort of independence and a supply chain question.

14:51And is your expectation that this is going to play out? And to what extent is it going to play out? The cloud, as we mentioned, there's a Chinese internet and the sort of Western, Western world internet. How widespread is this sovereign AI thing to go? Yeah. I'm going to borrow an analogy that Giro uses, which is that in the industrial revolution, you could look at where resources flowed, right? I think you should talk about how viewing it from the lens of oil reserves, you know, can kind of dictate which countries can and can't participate in the industrial revolution. Go ahead. So if you look at the industrial revolution, some oil was the foundation of a lot of the technology.

15:23So you needed all reserves in order to participate. And I think it'll be a little bit the same thing, right? If you want to build industry in a particular country, if you want to be able to export things, if you're going to be able to drive development, and if you want to have the hardest of power that comes with that, you need the corresponding reserves. And I mean, I think AI data centers are a little bit like these oil reserves. With a big difference being you can actually construct them themselves if you have the necessary investment, dollars, and the willpower to do it. But I think they will be the foundations for building all the layers on top.

15:53Right. And ultimately I think determine who wins this race. And in my mind, the countries that invest in building out the AI factories or in this sense, the oil reserves to borrow Gido's analogy, I think of them as one body of countries. Let's call them hyper centers, right? The idea is they're centers that have enough compute capabilities to compete at the frontier and run their own sovereign models, sovereign infrastructure. And then there's everybody else who just doesn't have the resources to do that. And if you look at after the Industrial Revolution, you could argue the next major technology revolution was the advent of modern finance, the Bretton Woods and IMF regime, where modern finance said, we're going to all use this one measure of value called the dollar.

16:33And you were either in a country that produced the dollars like America, or you were in a country that produced a lot of good, that acquired dollars like China. And then if you weren't in one of those two, you really had to figure out whether you aligned with one of these trade blocks or not. And what happened is you had countries like Singapore, Luxembourg, Ireland, and Switzerland who'd realized, well, we just don't have the resources to build out our own reserve system. And there's not that much by way of local production that we can do to acquire dollars. We can't really trade. So we've got to find a way to insert ourselves in the flow.

17:06Right. And so Singapore, of course, famously became the entry point for dollar flows into Asia because they invested a ton in rule of law and a great tax regime and sort of stable government and local corruption and all of that. Switzerland did something similar for European investments and European capital flows. So I think what we're watching right now is that build out where there's US and China which clearly have enough compute to be hyper centers. And then you've got folks like the Kingdom of Saudi Arabia saying, we want to be a hyper center. And if that means we've got to trade our oil to acquire large numbers of NVIDIA chips, we do that right now.

17:42And I think in that bucket, there's probably the Kingdom of Saudi Arabia, this Qatar, this Kuwait, there's Japan, Europe, clearly. And then I think the question is, everybody else, what do they do? And it's not clear to me what you have to do to become the Singapore of the eye. And maybe the Singapore of the eye ends up being Singapore because actually now they have an enormous sovereign wealth fund as a result of participating in modern capital flows. But I think a bunch of other countries are sitting around wondering, is this the time where we actually buy? Do we build? Do we partner? What do we do?

18:14Yeah. And talk more about the implications behind what this means. Is this something that the US should be excited about? What does this mean as we think about foreign policies? Are there winners across the board and all these local governments? We want Let me talk about some of the big implications here. Good, thank you, Stefan. Big structural revolution is both a threat and an opportunity. I think the United States and AI right now has the world leadership. Yeah. That's an opportunity. Hang on to it, won't be easy. Is it an every -take revolution? Don't we want people to be dependent on us in the same way that they were in the cloud revolution or do we benefit somehow from it being more decentralized?

18:48The world is not one place. So I think complete centralization won't happen. I think the leader is good. Having strong allies that also have that technology is also very valuable. So it's probably a balance of those that we're looking for. Yeah. To put a finer point on your last note, there's that you could think about a balance. They were clearly in an unstable equilibrium right now. Yeah. And so Gido's right that the arc of humanity and history such that things will shake out until there's a stable equilibrium. And so what is the stable equilibrium? And I think one way to reason about it is you could look at historical analogies.

19:21So post -World War II when Europe was completely decimated. There was a group of really enterprising folks in the private sector and the public sector who got together and said, hey, we can either choose to turn our backs on Europe and adopt a posture of isolationism, where we mostly focus on a post -war American -only agenda. Or we can try to adopt a policy where we know that if we don't help out our allies, somebody else will. And so they came up with this idea called a Marshall Plan, right? Where a number of leading enterprises in the US got together like GE and General Motors and literally subsidized the massive reconstruction of Europe that helped a lot of European economies quickly get back on their feet.

20:06And at the time, there was a ton of criticism of the Marshall Plan because it was viewed almost as an export of capital and resources. But what it didn't end up doing is then solidified this unbelievable trade quarter between the US in Europe for the next 50 years, which really kept China out of that equation for the 70 years. 70 years really. And so I think we have a choice either approach it the way we would the Marshall Plan for AI, right? And say, well, a stable equilibrium is certainly not one where we just turn our back on a bunch of allies because China definitely has enough of the compute resources to try to export great models like DeepSeek to the rest of the world.

20:43So what do we want our allies on Deep secret llama. That's what it comes down to at the model level of the stack, right? And I think that the realities that a number of countries are not waiting around to find out. That's why you have efforts like Mistral in the EU, right, where they are being approached by a ton of not just European nations, but a ton of other allies of Europe to say, hey, can you help us figure out how to build out our own sovereignty eye. And so I think we're about to see basically the single biggest build out of AI infrastructure ever because most of the purchase orders and the capital is being provided by governments because they realize this is a critical national need.

21:28And their stable equilibrium is certainly not to depend on somebody else or depend on an uncertain ally. And so the ones that certainly have the ability to fund their own sovereign infrastructure are rushing to do it right now. And what does that mean for the sort of nationalization debate or how you see that playing out? Leo Poldash and Brenner formerly of OpenAI and his famous sort of report talked about how, hey, if this thing becomes as critical to national security as we think it will be, at some point, the governments aren't just going to let private companies run it. They're going to want to have a much more integrated approach with it.

21:58Where do you stand with the likelihood of that? And what does that mean just in terms of the feasibility of regulation in a world where it's much more decentralized? And we already have this with DeepSeek. that every time you change the game in terms of wearing an arms race and you can't control everything. We're in the open source conversation as well. We're backing some of these players. We're your thoughts on where there's all the nets out. I think I have probably a strong opinion on that. I mean, I grew up in Germany, right? So benefiting from the Marshall Plan and also seeing how that pulled away Western Germany towards the United States.

22:25And eventually, he's still in Germany also towards the United States whenever I realized the impact of that. One lesson I took away from that is that I think any kind of centralized plan approach does not work. Eastern Germany is Western Germany is a nice AB test. Central planning versus a free market economy will work better, right? And I think the results speak for themselves. So I think basically having the government drive all of AI strategy, UNMENHETNSTYLE project, or poll project, pick your favorite successful project there, I can't see that working. You probably need a highly dynamic ecosystem of a large number of companies competing.

22:58There's some areas I think where the government can have a hugely positive effect, right? on the research side, we see it again, again, funding fundamental research, which is not quite applied enough yet for enterprises to pick up, right? It's very valuable. I think it can help in terms of setting good regulation. Bad regulation can easily torpedo AI as we've seen. And so I think there's a strong will for government to lead this and to direct this. There's no master plan at the end of the day that you can make that basically has all the details that has to come from the market. I don't agree with the Ash and Brenner point of view.

23:30I agree strongly with Gido that the history of centralized planning at the frontier of technology is not great. Barring a few situations that were essentially brief sprints of war, right, and arguably even the Manhattan Project, which is the analogy I think he uses in his piece, we now know that there were leaks. It was literally a cordoned off facility in Los Alamos or whatever, and they were still spies. And so if you're approaching this from the lens of the models or what are the equivalent of nukes. And we've got to regulate the development of these by locking up our smartest researchers in some facility in Los Alamos.