AI Is Becoming a Regional Race

Are we going to welcome this technology, or are we going to be hostile to its development?

AI has already percolated throughout society at one of the fastest diffusion rates of any general-purpose technology. Now the question everybody’s asking is: Do we build or buy?

My big idea is infrastructure independence, and it’s the idea that a lot of countries and regions are starting to realize that modern AI—you know, deep-learning-based AI, generative models—is a form of what’s been called general-purpose technologies. In the history of humanity, we’ve only had maybe 20 or 22 or so general-purpose technologies, like electricity and the printing press, that have very broad-based applications in society. They end up being largely horizontal economic multipliers and progress multipliers across a whole set of pillars and domains in society.

There are usually 2 moments in the adoption of a general-purpose technology when countries, nations, and states start asking, first, “Are we going to welcome this technology, or are we going to be hostile to its development?” That’s the first step that becomes pretty important in a country’s progression: Do we allow this in, regardless of whether we own it? Do we embrace it or not?

And then the second is: Do we build or buy? Can we trust somebody else to provide it for us? We’re well past the stage of asking whether we embrace it or not. Billions of people around the world have already embraced it, so the government doesn’t really have a choice, so to speak. In that sense, AI has already percolated throughout society at one of the fastest diffusion rates of any general-purpose technology, and now the question everybody’s asking is: Do we build or buy?

It’s probably the single largest purchasing decision that’s going to happen in the next 24 months. Do nation-states start buying? Do they build or buy?

I love the parallel of companies, because there are many companies that choose to build, but also companies that choose to rent or buy. As you think about that, there are large nations around the world, like the United States, which are clearly building. But talk about the argument for smaller nations—the 190-plus countries around the world.

The good news here is that we’ve got hundreds of years of human history to look at for clues about what happens next. If you were a small country in the early 1900s, watching the modern electrification of the developed world, and you chart what happened with many of those countries, many of them decided to enter into what were called joint-venture agreements.

It starts with a joint venture with a country that’s at the frontier. These countries at the frontier of AI are what I call hypercenters. These are countries that have the ability to develop, train, build, and host their own frontier models. I call them hypercenters mostly as an homage to the word hyperscaler, because there have been a handful of companies that have had the compute and the talent to actually build frontier AI.

Now what we’re seeing is a shift from just those companies driving a bunch of frontier AI to countries and regions driving it. If you’re a small country and you’re saying, “We certainly believe that it’s important to have our own AI infrastructure. We want to be independent, but we don’t have all the compute required to train these models, or we don’t have all the talent locally,” then what you enter into is a joint venture with a country or an overseas partner that matches your values.

This is the really important thing about AI models and how they’re different from infrastructure like electricity: There’s a fundamental encoding of human values in AI models, because they’re trained on data. The data has local norms and cultural values embedded in it. If you happen to train a model on a bunch of internet data collected in the U.S., the models are generally American. They’re encoded with that.

If you train a model on data in France, the models subtly have a bunch of different values encoded in them that reflect those cultural norms. I think step 1, if you’re a small country, is actually being crystal clear about which value systems you align with most among the hypercenters.

The way the internet worked out was that there essentially ended up being 2 internets: the Chinese internet and the rest of the world. AI may not end up looking that different. If you’re a small country, what you really have to figure out is whose values align more with yours.

A good historical precedent to look at here is the technology of money. Money is a pretty general-purpose technology, and what happened in the early 1900s with the modernization of finance is that a number of countries started to ask the same question: Do we build or buy our own currency? Do we rely on the dollar, or do we have our own currency?

That led to the modern-day currency regime, where the dollar is a single global reserve currency. It happened through a bunch of allied cooperation, where a number of countries realized they did not have the local resources required to hold the peg of gold to the dollar.

You have the U.S., China, and India, and then you have a number of smaller countries that decided they wanted to be flow points. You have Singapore and Ireland, and you have Luxembourg and Zurich, which became massive global leaders in modern finance because they decided they wanted to ally with one of those power centers.

So, if you think about the AI world, let’s call the regions at the frontier hypercenters. Then we have compute deserts—places that have literally no installed base of compute capacity to even be relevant. All the smaller countries have to figure out which of the hypercenters they want to align with, and how to become a modern-day Singapore, Ireland, Luxembourg, or Zurich for the world of AI infrastructure.

It starts with deciding whether you want to be a compute desert or not. If you’re not, and you’re going to embrace AI infrastructure as a government, then I think you’ve got to figure out which hypercenter you want to align with most. Then it becomes quite easy to reason about how to be a valuable ally.

That’s such a good parallel, because a lot of people think about resources in terms of the farmland that you have and the people who are working in that economy. But what you’re pointing out is that countries have, for a long time, offered value or a resource in other ways.

As we think about AI, there are a few things that you’ve pointed out that countries can invest in, whether it’s the compute capacity they have, the energy resources to power AI, or forward-thinking policy. Maybe we can break down each of those. How do you think about each of those blocks, and how countries should be maneuvering or investing in those things?

The good news is that there are only 3 or 4 ingredients here that really matter. The first is compute, which we’ve talked about. The second is abundant and low-cost energy, which powers the data centers. The third is data—the availability of really high-quality tokens for these models to learn on. The fourth is regulation.

The bad news is that the world is pretty unevenly split up. Some countries have dramatically more compute than others. Others have dramatically more energy than others because of their natural reserves.

If you’re in the Middle East, you may not have massive data centers yet, but what you do have is vast reserves of oil. How you translate that into becoming a hypercenter is the law of comparative advantage: You’ve got energy, so you should use that to attract the world’s best teams, companies, foundation-model labs, and so on, by trading what you have with what they have.

I’m quite bullish on allied ties between countries that recognize what their strengths are and then partner with other countries to fill that gap. By countries, I mean private companies, too, from other countries. One of the things we may end up seeing is jointly trained models between countries.

For most countries, it’s impossible to have total infrastructure independence across all parts of the stack. What is much more feasible is to be great at one part of the stack and then collaborate with another sovereign, country, or region to achieve joint independence from a value system that you don’t subscribe to.

In the long term, you might be able to build things out, but infrastructure of this kind can often take years, if not a decade, to scale. As an example, lower down in the stack from the model layer, you have the chip layer, and even below that you have the lithography layer.

There’s a company in Holland called ASML that builds literally the world’s most important machines. How many machines do they make per year? It’s some very small number. Each machine costs about $200 million, and they’re the only company that can do lithography at this level of precision.

Is it feasible for the U.S. to say, “We’re going to build our own ASML tomorrow”? No. It’s just going to take 10-plus years. EUV lithography takes a really long time.

On the other hand, is it feasible for a smaller country to say, “We’re going to build and train our own local models at the frontier”? That’s a little bit easier to do over a months-to-quarters timescale if you’ve got a leading research team—and that’s a big if. There are only a handful of research teams globally that are capable of this.

To answer your question, I don’t think sovereign AI or infrastructure independence means you have 100% ownership over every part of the stack. That’s infeasible over the short term. It means that you don’t rely on somebody for a critical part that you don’t trust.

Can we talk about private companies for a second? You’ve brought them up a few times. How do you think about that dynamic where, as a nation-state, you’re saying, “We need this sovereignty,” but at the same time, can you rely on that sovereignty through the companies that exist within your nation?

Using America as an example, does the government really need to be involved, or can it just let Anthropic or OpenAI command that part of the stack? How do you think about the difference between government and private enterprise?

The line is pretty stark in a few countries and more blurry in others. In China, the line is very clear. There’s a law called the PRC’s 2017 National Intelligence Law that says Chinese individuals and entities are required to support PRC national intelligence work by law. That means if there’s any technology that a PRC company has access to, it’s automatically obliged to make that available to the government.

That’s not the case in the United States. There are some covered types of technology, like dual-use technology or classified defense technology, where, if you’re developing it—particularly if you’re funded under a defense program—then you’re required to make that available to the government, because the government is paying for the development of that technology.

But by and large, the private sector in the United States and most other allied countries is, by default, protected from having to make its technology available to the government. That’s not the case in the CCP.

I think the question for most countries becomes: Where on that spectrum do you want to exist? Every country approaches it slightly differently, but the Five Eyes—the U.S., Canada, the U.K., Australia, and New Zealand—generally have a joint approach or framework for categorizing this infrastructure.

By and large, AI models have not been categorized as dual-use or protected under national security.

The history of technology has largely shown that, if you’d like to win, unlocking the best talent of a country with as few bureaucratic slowdowns as possible usually ends up winning.

If we think about wanting to keep America at the frontier, and we think about the different layers or ingredients that we talked about earlier, are there any high-risk areas where you think we’re falling behind? It could be in the energy department, at the model layer, or in the fact that we’re dependent on ASML. Going down the line, is there anywhere where you think we’re at high risk of not keeping our edge?

I think we go back to the 4 ingredients we talked about earlier at the frontier of AI: compute, data, energy, and laws.

On the compute front, I think the private market in the United States is doing a pretty good job. It’s pretty responsive to market demand, and I think there’s no coincidence that the largest infrastructure businesses in the United States are chip companies and computing companies. We’ve generally done a pretty good job of letting the market feed that demand.

On the data side, things are extraordinarily tough. The Biden executive order last year was a starting gun that said, “AI is important. Please do something about it,” and left it to the states to figure it out. The states have all taken a complete patchwork of approaches to data regulation.

In 2024 alone, I think there were more than 700 pieces of state-level legislation that were AI-specific. A bunch of those laws, if you look at them, are really well-intentioned but atrociously implemented ideas for data regulation—impossible to adhere to, basically.

I think one area where we’re handicapping ourselves is that there’s no unified framework in the United States at the federal level yet for data, especially around training. We needed that yesterday.

Overseas, in a number of countries where the rule of law, especially on copyright and IP, is just less stringent, those labs are happy to race ahead, whereas our companies here are trying to figure out what they should even comply with. That hurts you more than a laissez-faire approach.

I think our companies would be totally fine with the United States being compliant. The best founders at the frontier would be fine with that. They just want to know what to comply with, rather than having 50 different states with different regulations that are changing, unclear, and in some cases impossible.

There’s also a fundamental scientific problem: There are very real data walls that these models run into. One of the things that hurts frontier research in the United States and allied countries is a lack of government support for collaborating across borders to make more data available to allied regions.

On energy, I think we’ve obviously hamstrung ourselves in the United States with nuclear. France’s embrace of nuclear 20 years ago has positioned it to have extraordinarily efficient data centers today, whereas in the United States, I think we’ve basically shot ourselves in the foot around that.

Lastly, around inference regulation, I think what we’re not doing enough of is making it clear where liability rests. I’ve seen a number of proposals ahead of legislative sessions next year that want to hold model developers liable for the outputs of inference, even if the misuse is being done by somebody else.

What does that do? It drives those very important developers elsewhere, essentially forces most startups to lose much-needed ground to big tech companies, and entrenches incumbents even more.

This idea is truly global. What are you looking out for—or what should a legislator or the head of a nation be thinking about in some of these decisions? Are you looking for countries that are buying GPUs or building out new energy centers? What are you paying attention to as indicators?

Definitely compute. If you think about the AI supply chain, the first mile starts at the data center. That’s the new atomic unit of sovereignty, I would say, which is a new thing. We’ve never actually had nation-states think about an AI data center as an atomic unit that countries should be purchasing.

About 24 months ago, we started seeing nations reason about that first mile as being important. We’ve seen an enormous amount of NVIDIA purchase orders come from the balance sheets of governments—unprecedented demand from nation-states realizing that they want to be hypercenters.

That starts with placing orders 12 to 36 months in advance to take delivery of GPUs, because if you don’t get in front of that line, it’s over. You’re getting them after everybody else.

The second thing I look for is founders who are deeply technical, often come from deep research backgrounds, and have already led frontier-model development, frequently inside large hyperscaler labs. An example is Arthur Mensch, who started Mistral AI; he worked at DeepMind. Or Guillaume Lample, who led the initial Llama family at Meta.

There’s a new class of founder who’s primarily technical, has their training in academia, and is motivated to solve all the really hard problems that come with delivering infrastructure for large nation-states and regions. If you’re a founder like that who has the guts, then your impact on humanity ends up being quite generational.