What does it take to become an AI-native nation?

As AI systems move from lab experiments to infrastructure powering everything from customer support to defense, countries are racing to embed AI into the very foundation of their economies.

But the most serious players aren’t just adopting AI tools — they’re redesigning how they govern, invest, and scale. These are the early AI-native economies: nations building sovereign compute infrastructure, launching AI-focused universities, crafting regulation with global reach, and embedding automation across sectors like healthcare, logistics, and energy.

In 2024 alone, we’ve seen generational-scale commitments unfold. The United Arab Emirates invested heavily in building its own large language models and expanding national compute capacity via G42 and its Falcon model initiative. Singapore joined a $30 billion partnership with Microsoft, BlackRock, and Temasek to create AI infrastructure across Asia. The European Union passed its landmark AI Act while simultaneously channeling hundreds of millions into startups like Mistral AI, which aims to create open-source sovereign models.

This isn’t just about training bigger models. It’s about building national capability — shaping the future of economic growth, industrial competitiveness, and digital sovereignty.

This blog explores how countries are positioning themselves in the post-model era. From the Gulf to Europe to Southeast Asia, we break down which regions are leading, how they’re doing it, and what it means to build an AI-native economy today.

What Is an AI-Native Economy?

To understand how countries are competing in the AI-native era, we first need to define what “AI-native” means, and how it differs from simply being AI-enabled.

An AI-native economy doesn’t just adopt AI tools. It systematically integrates AI into the core of public infrastructure, industrial policy, education systems, and national strategy. These are economies where AI isn’t an add-on; it’s a foundational layer shaping how services are delivered, how citizens interact with the state, and how businesses operate.

While most countries today are AI adopters, AI-native nations are builders. They invest in sovereign compute, develop local models, attract global AI talent, and regulate AI systems on their own terms. They move beyond relying on foreign APIs or hyperscaler platforms, and instead establish control over critical infrastructure, intellectual property, and deployment norms.

The Stanford Institute for Human-Centered Artificial Intelligence (HAI) calls this new phase the “post-foundational model era,” where “governments and enterprises are no longer just users of AI, they are becoming AI-native themselves” (Stanford AI Index 2024).

Key features of an AI-native economy include:

• Sovereign Infrastructure: Investment in national cloud platforms, GPU clusters, and model training pipelines that are not dependent on foreign tech stacks.

• Strategic Regulation: Purpose-built frameworks like the EU AI Act that shape model deployment, bias mitigation, and risk classification.

• Talent Pipelines: National programs to train AI engineers, scientists, policy experts, and builders, such as the UAE’s Mohamed bin Zayed University of Artificial Intelligence, the world’s first graduate-level AI university.

• AI Across Sectors: Use of AI in healthcare, energy, manufacturing, defense, agriculture, and logistics, not just in productivity apps or chatbots.

• Public-Private Alignment: Governments, research institutions, and startups working together to develop models, use cases, and industry standards.

In short, AI-native economies don’t just consume innovation, they set the pace of it.

How Nations Are Positioning to Win the AI-Native Era

AI is now a matter of national advantage. Around the world, governments are going beyond pilots and partnerships, they’re laying down AI-native infrastructure, writing regulatory frameworks, and embedding AI into the core of their economies.

Some lead with compute. Others with regulation. A few, with speed. But what unites them is the belief that controlling AI means controlling future growth.

In the sections below, we’ll look at how the Middle East, Singapore, Europe, and the U.S./China are building toward that goal, each in their own way.

The Middle East: Building AI Capacity as State Strategy

Few regions have moved faster, or more deliberately, than the Middle East when it comes to AI infrastructure and sovereign capability. At the center of that momentum is the United Arab Emirates.

The UAE isn’t just funding startups. It’s building national AI models and compute from the ground up. G42, a government-aligned technology group, has partnered with the Advanced Technology Research Council and developed the Falcon series of open-source LLMs, which rival top global models in performance. These models are trained on sovereign data, using locally controlled infrastructure — a clear move toward AI independence.

More recently, the UAE launched AI71, a startup with backing from G42 aimed at monetizing Falcon models and offering data-hosting capabilities within the country’s jurisdiction. The initiative reflects a broader strategy: develop AI in-country, host it on UAE soil, and export it globally — on Emirati terms.

Saudi Arabia is taking a similar route. Its SDAIA (Saudi Data & AI Authority) has rolled out national AI policies and partnered with global players like Huawei and SenseTime to accelerate domestic AI deployment. In 2024, it committed over $40 billion through its Public Investment Fund to develop a sovereign AI ecosystem, including chip manufacturing, cloud infrastructure, and enterprise automation.

Education and workforce development are also top priorities. The UAE launched the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), the world’s first graduate-level, research-based AI university. Saudi Arabia, meanwhile, is training thousands of civil servants and engineers in AI literacy through programs tied to its Vision 2030.

Across the region, the goal is clear: don’t just use AI, own the stack, build local talent, and compete globally.

Singapore: Infrastructure as Economic Leverage

Singapore has always played the long game when it comes to technology policy, and AI is no different. The city-state is betting on national infrastructure, international alliances, and trust-building as its way into the AI-native era.

In June 2025, Temasek, Singapore’s state investment firm, joined forces with Microsoft and BlackRock to back MGX, a $30 billion initiative to build data centers, AI infrastructure, and digital ecosystems across Asia. The deal is one of the largest public-private investments in the region’s AI stack, and positions Singapore as the launchpad for distributed compute and model training in Southeast Asia.

Beyond funding, Singapore is also exporting standards. Through initiatives like the AI Verify Foundation, the country is building open-source testing tools for AI governance and safety. These tools are being adopted by companies around the world and serve as a counterweight to the “regulation through dominance” model used by large tech platforms.

Its national strategy prioritizes AI in key verticals: urban planning, healthcare, logistics, and education. In healthcare, for instance, Singapore is scaling diagnostic models and robotic systems in public hospitals through its Smart Nation initiative, while also embedding AI in public services like transportation and housing allocation.

Singapore’s approach is less about building the biggest model, and more about creating the infrastructure and rules for others to build responsibly on top of it. In a region caught between U.S. and Chinese influence, that’s a strategic play.

Europe: Sovereign AI and Regulation at Scale

If the Middle East is building AI infrastructure, and Singapore is exporting standards, Europe is doing both, and adding regulation into the mix.

In March 2024, the European Parliament passed the AI Act, the world’s first comprehensive law governing artificial intelligence. It sets out strict rules for high-risk systems, demands transparency for foundation models, and gives EU regulators the power to fine violators up to 7% of global revenue. The law effectively establishes Europe’s model for AI accountability, and positions it as a global norm-setter.

But Europe isn’t just regulating. It’s building too.

France is leading a new wave of AI-native investment. Its startup Mistral AI raised over $640 million in less than a year to develop open-source foundation models trained on European data. Germany’s Aleph Alpha and Spain’s IberIA project are building language models tailored to local languages and regulatory environments.

The EU is also ramping up public infrastructure. The EuroHPC Joint Undertaking, a consortium of EU countries, is investing billions in high-performance computing (HPC) clusters — many of which are now being upgraded to support generative AI training. Through programs like DIGITAL Europe, the EU is also funding AI testing centers, ethics labs, and training initiatives.

Crucially, Europe’s strategy emphasizes sovereignty. The ability to build, host, and audit AI systems within European borders is seen as essential to digital independence, especially as reliance on U.S. and Chinese tech firms deepens elsewhere.

In the AI-native playbook, Europe is proving that strategic regulation, infrastructure control, and open-source collaboration can coexist, and scale.

The U.S. and China: Big Tech vs. State-Led Scale

The two AI superpowers, the United States and China, are taking vastly different but equally ambitious paths to dominance. What sets them apart from other regions is scale: of talent, of data, of compute, and of capital.

In the United States, the AI-native transformation is largely industry-driven. Companies like OpenAI, Anthropic, Google DeepMind, and Meta are leading global benchmarks in foundational model development. OpenAI’s GPT-4 and Anthropic’s Claude 3 are widely considered among the most capable general-purpose models, while Meta’s LLaMA series is shaping the open-source landscape.

What makes the U.S. AI ecosystem powerful is the combination of venture capital, research freedom, and hyperscale infrastructure. Firms like Microsoft, Amazon, and Google own and operate some of the world’s largest GPU clusters, enabling continuous training and deployment of frontier models. The White House’s Executive Order on AI in late 2023 added momentum by requiring safety disclosures, watermarking standards, and red-team testing from top AI firms.

But AI-native infrastructure in the U.S. is still mostly private, a contrast to China, where the state is leading the charge.

China’s Ministry of Science and Technology recently designated 18 national AI pilot zones to accelerate deployment in smart cities, manufacturing, and defense. Companies like Baidu, Alibaba, and Huawei are developing LLMs for Mandarin and other regional languages, while Beijing is investing in chip sovereignty through initiatives like the Guoxin Project.

The contrast is clear: America’s AI-native playbook is driven by open markets and private platforms. China’s is top-down, with centralized funding, planning, and enforcement. Both are pushing hard toward the same goal, to define the next global infrastructure layer.

And both are shaping the environment other countries must now navigate.

The AI-Native Nation Toolkit: What the Leaders Have in Common

Despite their differences in governance, resources, or ideology, the countries leading the AI-native transition share a striking set of priorities. These priorities now form the blueprint for what it means to be AI-native at the national level.

1. Sovereign Compute and Infrastructure

Access to GPUs and model training infrastructure has become a strategic asset. Nations like the UAE and Singapore are investing in data centers, national AI clouds, and sovereign compute to ensure they aren’t locked into dependencies. Europe is expanding its EuroHPC network, and even the U.S. has launched the National AI Research Resource to democratize compute access for research institutions. Compute is no longer just a tech issue, it’s a national capacity question.

2. Workforce and Education Pipelines

Every AI-native economy is addressing talent, not just with immigration or outsourcing, but through targeted investments in education. The UAE’s MBZUAI is training postgrads in applied AI fields. Singapore’s AI Apprenticeship Programme is turning mid-career professionals into ML engineers. And the EU’s Digital Europe Programme is funding AI curricula across member states. The fastest-growing demand? AI-literate generalists, people who can connect policy, product, ethics, and engineering.

3. Governance and Regulation

Leading economies are no longer waiting for consensus to regulate AI. Europe passed the AI Act, setting the world’s first binding rules on high-risk systems and foundation models. Singapore created AI Verify to test transparency and fairness. Even the U.S., typically slow to regulate tech, issued a sweeping Executive Order on AI in 2023. AI-native countries don’t see regulation as a brake, they see it as infrastructure.

4. Open Models and National IP

A growing number of countries are developing their own foundation models. The UAE has Falcon. France has Mistral. China has Baidu’s Ernie Bot. These efforts serve a dual purpose: retaining national control over capabilities and capturing value from IP. Many nations now see open-source models as a path to both sovereignty and competitiveness, reducing reliance on proprietary APIs from foreign tech firms.

5. Strategic Industry Deployment

AI-native economies are embedding AI into national priorities: energy, healthcare, education, logistics, and defense. In Saudi Arabia, AI is used to optimize energy grid performance. In Europe, governments are deploying LLMs to streamline immigration and legal services. In Singapore, AI helps manage public housing and transportation. This isn’t “AI for AI’s sake”, it’s AI as a public utility.

Conclusion: Why AI-Native Is a National Strategy — Not Just a Company Goal

In the past, digital transformation was mostly a corporate agenda. Governments were adopters, not originators. But in the AI-native era, that dynamic has flipped.

Today, countries are not just investing in AI, they are reorganizing around it. They are building national models, funding sovereign compute, drafting global governance standards, and reshaping labor markets to support an AI-driven future. The AI-native economy is not something governments are watching from the sidelines. It’s something they’re actively designing.

This shift reflects a deeper truth: AI is no longer just a technology layer. It is fast becoming an economic operating system, one that governs how value is created, distributed, and defended.

In this new world, having access to foundation models or LLMs isn’t enough. What matters is whether a nation can produce, host, adapt, and govern those models on its own terms. Whether it can train its workforce to use them responsibly. Whether it can channel them into real productivity, not just demos.

Countries like the UAE, Singapore, France, and the U.S. are already treating AI as critical infrastructure. They’re developing AI-native strategies not just to compete with each other, but to future-proof their societies.

For businesses, this signals a massive shift. The biggest customers, regulators, and accelerants of AI adoption will increasingly be governments. And for governments, the AI-native playbook is becoming a new metric of sovereignty, one that may matter more than GDP or military strength in the decades ahead.

The question is no longer: Should we adopt AI?

It’s: Can we build an AI-native economy before it’s too late?