Geopolitics of AI: Why Nations Are Building Their Own Models

"They're cultural infrastructure. They'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 post-training steps you add that steer the models to say something or not, to refuse the user or not."

""DeepSeq has forced people to update their priors, where just a year before DeepSeq came out, a number of tech leaders in Washington testifying that China was five to six years behind the U.S., with confidence on the record. And then DeepSeq comes out 26 days after OpenAI puts out the frontier. I mean, it just shattered all of those arguments. So the calculus has changed. And I think it means that the only way to win is build the best technology and out-export anybody else.""

""Because these models, like we discussed earlier, are cultural infrastructure, you don't want to be colonized in the digital era. In cyberspace. And I think that's pretty spot on. Instead of colonization, what we have is now, I think, foundation model diplomacy. That's a good way to put it. I think that's right. It suits the U.S.'s relative skill sets, which is innovation and working with our allies.""

""In the world of AI, you can't get the kind of control most enterprises want without having access to the weights. And at the time, the only real comparable model to the frontier closed source was LLaMA. And then the creators of LLaMA left to start Mistral. So it was a pretty natural decision. I think since then, there's a different thing that's turned up, which is the idea of sovereign AI infrastructure that's not just models. It's everything else down and up.""

""I think now we're in the process of building yet another building block, which is the model or AI, whatever it may be called in the end. So it's a fourth pillar in a sense. Compute network storage has become a compute network storage model. And in that kind of world, a cloud needs to provide all four. Right.""

""Even for sophisticated teams like OpenAI, I mean, they've literally rolled back an update to ChatGPT, I think, three days ago. They called it the sycophancy update, where they crafted the wrong reward model. Well, it turns out crafting the right reward model is really hard. An area where we're seeing a ton of progress is reinforcement learning, where if you craft the right reward model, the agent gets much better at accomplishing that task.""

"They're not being called AI data centers. They're being called AI factories. In the Industrial Revolution, having oil was important. And now having data centers is important. These models aren't just compute infrastructure. They're cultural infrastructure. It's not just sort of defining the culture, but sort of controlling the information space."

"The Kingdom of Saudi Arabia announced plans to build its own AI hyperscaler called Humane. But they're not calling it a cloud provider. They're calling it an AI factory. And that language alone suggests a shift."

"Nations no longer want to outsource their most strategic compute. They're building sovereign AI infrastructure, factories for cultural and computational independence."

"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."

"Because if you x-rayed an app like ChatGPT, sure, on the surface, it looks like nothing much has changed, right? It's still a chat box. You type in what you need and it outspits an answer relative to 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. 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, to 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. That starts to look more, people use the word agent, right, to call it that."

"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, right, where it's transmitting telemetry, that's actually very hard to eval or benchmark 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. It 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 adversarially until it's too late, this is a massive vulnerability."

"If you want to build industry in a particular country, if you want to be able to export things, if you want to be able to drive development, and if you want to harness the 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 yourselves 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 that ultimately, I think, determine who wins this race."

"So 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 the Marshall Plan, right? Where a number of leading enterprises in the U.S. 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."

"I think basically having the government drive all of AI strategy, you know, Manhattan-style project, Apollo 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. There's some areas, I think, where the government can have a hugely positive effect, right? On the research side, we've seen it again and 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."

Why are countries building sovereign AI factories instead of relying on existing cloud providers?

Countries are seeking computational and cultural sovereignty in AI due to strategic, legal, and cultural reasons. Existing cloud providers, dominated by U.S. and Chinese firms, represent centralized points of control that nations increasingly distrust for hosting sensitive AI workloads. Sovereign AI factories give countries direct control over infrastructure, AI models, and governance, preventing adversarial influence and enabling alignment with local norms and regulations. This shift also responds to regulatory fragmentation such as GDPR, which demands data locality and compliance enforcement at a regional level. Such factories are optimized for AI-specific infrastructure requirements, including GPU-heavy workloads, energy, and cooling.

What distinguishes AI factories from traditional data centers?

AI factories are specialized infrastructure designed primarily to handle AI workloads characterized by intensive GPU compute, high energy demands, and advanced cooling requirements. Unlike traditional data centers optimized for CPU-centric or general cloud workloads, AI factories integrate hardware and software tailored for training, inference, and post-training controls of AI models. They also emphasize proximity to power sources and strategies for locking in energy supply. Moreover, they embody a conceptual shift, being treated as cultural infrastructure that supports sovereign AI ecosystems rather than generic compute facilities.

How do sovereign AI models impact cultural and information sovereignty?

Sovereign AI models embed the cultural values and norms of their originating countries through the data they are trained on and the policies that govern their inference behaviors. By controlling AI locally, countries can shape the information landscape, influence public opinion, and enforce jurisdictionally appropriate behavior from AI systems. This control extends to steering models post-training to avoid or promote certain narratives, thus exerting a form of soft power. Loss of such control risks reliance on foreign cultural norms embedded in adversarial AI, which may conflict with national interests and social values, posing political and societal challenges.

What are the geopolitical risks associated with relying on foreign AI infrastructure and models?

Dependence on foreign AI infrastructure and models poses risks related to national security, economic autonomy, and cultural influence. If mission-critical sectors like defense, healthcare, and finance rely on externally controlled AI, adversarial nations might gain leverage or cause disruptions. Additionally, foreign models may embed biases or information controls misaligned with local values, affecting societal trust and governance. The inability to audit or influence such AI systems can create vulnerabilities and uncertainty. These risks drive nations to invest in sovereign AI capabilities to safeguard autonomy and resilience.