The Rise of AI Nationalism

Why Countries Are Racing to Build Their Own Compute Power — and What It Means for NVIDIA

The New Oil Is Compute

When NVIDIA CEO Jensen Huang revealed that Saudi Arabia is building AI factories — massive GPU clusters powered by the country’s abundant energy reserves — it wasn’t just a story about regional innovation. It was a signal. The race to dominate artificial intelligence has entered a new phase, one where compute power is no longer just a tool for startups and cloud companies — it’s a pillar of national sovereignty.

We’ve seen this playbook before. In the 20th century, it was oil. In the 21st, semiconductors. Now, in the 2020s, it’s AI infrastructure: the data, models, and—crucially—the silicon that powers them. And at the center of this shift is a new kind of nationalism. Call it AI Nationalism — a movement where nations seek to own their own algorithms, train their own models, and build their own AI stacks from the ground up.

The stakes? Everything from economic competitiveness to national defense.

What Is AI Nationalism?

AI Nationalism is the belief that artificial intelligence capabilities—particularly those related to language models, data processing, and autonomous systems—are too important to outsource.

At its core, this is about control:

Control of training data
Control of model behavior
Control of compute infrastructure, especially GPUs

Rather than relying on U.S.-based cloud giants or public APIs, countries are increasingly choosing to build their own stacks, invest in local infrastructure, and create “sovereign” alternatives. This trend isn’t about rejecting AI — it’s about reclaiming its foundation.

And in 2024, that foundation starts with compute.

Compute Is the New Battleground

In AI, “compute” doesn’t mean computers—it means high-performance chips capable of training and running large models. These include:

GPUs (like NVIDIA’s H100)
TPUs (Google’s Tensor chips)
Specialized AI accelerators

Without this compute layer, all the data and algorithms in the world are inert. You can’t train a ChatGPT-style model on a laptop. You need a warehouse full of optimized silicon—and the power infrastructure to run it.

This is where NVIDIA ($NVDA) enters the story.

Saudi Arabia’s AI Factories

The Kingdom of Saudi Arabia is building a national-scale AI infrastructure stack—GPU clusters, data centers, and custom model pipelines—all powered by its own energy supply. According to Jensen Huang, these “AI factories” are not just experiments. They’re strategic investments designed to make Saudi Arabia a major AI player.

Here’s why this matters:

Energy + capital + ambition = a potent combination
Saudi has the resources to scale, and it’s choosing to spend them on AI
These clusters won’t just serve local needs — they’ll train sovereign models in Arabic, build defense systems, optimize logistics, and more

The model is clear: if AI is going to shape the global economy, then owning the ability to build it is non-negotiable.

Who Else Is Joining the Race?

Saudi Arabia is far from alone. Around the world, nations are rushing to control their piece of the AI value chain:

🇺🇸 United States

Export controls on AI chips to China
CHIPS Act incentivizing domestic semiconductor production
Companies like OpenAI, Anthropic, and Cohere still rely on NVIDIA-powered infrastructure

🇨🇳 China

Racing to build domestic GPUs (e.g., Biren, Huawei Ascend)
Large LLM initiatives from Baidu, Alibaba, and Tencent
Government mandates for independent model training in critical sectors

🇪🇺 European Union

Focused on AI regulation (AI Act) and data privacy (GDPR)
Slow to build chip infrastructure, but pushing for sovereign cloud platforms

🇮🇳 India & 🇦🇪 UAE

Partnering with U.S. and European companies to build shared AI compute centers
Focused on regional language models, healthcare, and logistics

This isn’t just innovation—it’s infrastructure competition, with every country trying to own the rails of the next economy.

NVIDIA: The Geopolitical Winner

The common denominator? NVIDIA sells the picks and shovels for this AI gold rush.

Its GPUs—particularly the H100—are the default platform for:

LLM training and inference
Scientific computing
AI-native applications

Whether you're Saudi Arabia building an AI factory, or a startup in San Francisco training a healthcare model, odds are you’re buying NVIDIA.

And that’s the genius of NVIDIA’s position:

Geopolitical neutrality — everyone buys from them
Software moat — CUDA ecosystem makes switching costly
Supply scarcity — demand consistently exceeds capacity

Every new nation that joins the AI nationalism wave becomes a customer.

Risks and Friction Points

But this landscape isn’t without risk:

TSMC dependency — most NVIDIA chips are fabricated in Taiwan
U.S. export restrictions could push countries toward black market chips or homegrown alternatives
AI stack fragmentation — with multiple sovereign models, standards may diverge

Still, these risks may take years to fully materialize. In the short to mid-term, demand for NVIDIA’s hardware and platform will likely accelerate.

Investor Takeaway: Bet on the Infrastructure

You don’t need to pick the winning country. You don’t even need to pick the winning LLM.

In a world where every government wants to build its own AI future, the smartest play may be to invest in the company that supplies them all.

Right now, that’s still $NVDA.

The only question is whether anyone can catch them.

Nationalism Is Just the Start

The idea that compute equals power is no longer theoretical. Nations are beginning to act on it. Some will build massive clusters. Others will nationalize data. Still others will design their own chips and models.

But the trend is clear: the AI stack is becoming political territory, and countries want to own it.

That’s the rise of AI nationalism.

And it’s already reshaping the future of infrastructure, innovation, and investing.