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Inside the Global Wave of National Sovereign AI Models

Countries are turning to US infrastructure and Chinese models to reduce their dependence on both. The contradiction is real—but so is the control they can gain.

September has brought fresh momentum to AI beyond the US and China. On September 3, Saudi Arabia’s HUMAIN, backed by the Public Investment Fund (PIF), unveiled humain-m3, an Arabic-language model it commissioned from MiniMax. Days later, France’s Mistral AI raised €3 billion at a valuation exceeding €21 billion, funding model development and computing infrastructure. The Center for a New American Security (CNAS) calls this sovereign AI’s second wave.

Coverage of AI as a US–China contest often overlooks what everyone else wants. Governments increasingly hope domestic companies and infrastructure can reduce their dependence on foreign suppliers. The EU has proposed its European Technological Sovereignty Package. Canada has a sovereign computing strategy. Chile is leading the regional Latam-GPT initiative.

An international wave of AI nationalism is taking shape. Governments want personal data kept within their borders and models that understand local languages, laws and norms. They also fear that AI will reinforce the internet economy’s concentration of wealth and power.

Yet existing projects reveal a paradox. Sovereign AI can give countries more control while embedding American and Chinese technology more deeply in their economies. America’s approach is more forceful; China’s often involves a softer sell. Neither delivers complete independence, but partial autonomy still matters when access to AI can become a geopolitical bargaining chip.

Three questions explain the tension:

• What drives sovereign AI: language, the internet and economic influence.

• What countries are building: customized models and data centers.

• How influence spreads: America’s harder approach and China’s softer one.

Why Sovereign AI? Language, the Internet and Economic Influence

Nvidia CEO Jensen Huang helped popularize sovereign AI as a market for chips and servers. It has since become a political and security priority. Before examining those ambitions, though, consider a simpler question: Why would a country need an AI model of its own?

Often, the answer is language.

Japan’s Sakana AI develops technology adapted to Japanese needs. Mistral supports French; HUMAIN targets Arabic speakers. India’s Sarvam AI builds for Hindi and other Indian languages.

General-purpose models have historically relied heavily on English-language training data. Their apparent fluency elsewhere can conceal awkward phrasing and gaps in cultural understanding. Chinese users who tried ChatGPT in 2023 immediately noticed that its Chinese answers felt like Chinese written with English logic. Each paragraph made sense on its own, but the full response felt deeply strange.

The problem extends beyond translation. A shortage of high-quality local material can leave models poorly equipped to interpret legal terms, everyday assumptions or regional references. More speakers do not automatically mean more usable training data.

Web scraping alone cannot close every gap. Countries need access to local archives, institutional records and specialized datasets, alongside infrastructure for processing sensitive material under domestic rules. What I think of as a country’s “sovereign internet”—its language communities and locally generated information—becomes a resource for sovereign AI. Common Crawl’s language statistics illustrate how unevenly the public web represents different languages.

The larger motivation is economic. Many governments watched the internet boom enrich foreign platforms without producing comparable champions at home. They see AI as another chance to secure a greater share of digital activity.

The US and China dominate the platform economy. Japan, South Korea and India have substantial domestic businesses, but fewer platforms with comparable international reach. That imbalance now shapes who supplies AI—and who can withdraw it.

June’s US restrictions on Anthropic’s newest models, followed by Reuters’ July report that Beijing was considering restrictions of its own, made that vulnerability tangible. Chip controls add another layer.

The common objective is straightforward: recover some control over how models are developed, supplied and used.

What Countries Are Building: Customized Models and Data Centers

By mid-2026, CNAS tracked sovereign AI initiatives in 67 countries and the EU, up from 16 governments in 2023. New participants included Cambodia, Egypt and Pakistan. This was no longer exclusively a wealthy-country project.

A national model is often the most accessible starting point.

On September 2, Spain’s Multiverse Computing launched Quasar 438B, an English–Spanish reasoning model with a 1M-token context window. Its announcement cited a score of 43 on Artificial Analysis’s Intelligence Index v4.1.1, claiming the highest European result in that comparison. But Quasar is based on GLM-5.2, developed by China’s Z.ai. Its contribution is compression and more efficient deployment, not an entirely independent foundation.

Other projects follow a similar pattern. Japan’s Rakuten AI 3.0 uses the DeepSeek-V3 architecture. AI Singapore’s Qwen-SEA-LION-v4 builds on Alibaba’s Qwen.

CNAS finds that most tracked model projects with disclosed foundations adapt foreign open-weight models. Meta’s Llama remained the most common base by mid-2026, followed jointly by Mistral and Google’s Gemma.

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