Governments around the world are making grand declarations about their ambitions in artificial intelligence. Strategies are unveiled, taskforces formed, and glossy visions laid out in conference halls. Yet one truth remains stubbornly ignored: you cannot develop indigenous AI capacity without giving your next generation of thinkers—the students—the raw compute power they need to learn, experiment, fail, and build again.

Put plainly: if a country is not institutionalizing compute access for its students, it is not serious about AI.

For all the rhetoric about economic transformation and global competitiveness, governments continue to treat compute like a luxury commodity—locked behind grant applications, corporate partnerships, or the sheer luck of attending a well-funded university. But AI is no longer a specialized research niche. It is becoming a general-purpose technology, as fundamental to modern innovation as electricity once was. You would not tell engineering students to design turbines without tools. You would not tell chemistry students to innovate without a lab. Yet this is exactly what we are doing with AI: asking students to compete in a global race armed with nothing but laptops that can barely fine-tune a model, never mind train one.

Real leadership means building national compute infrastructure accessible to every student who wants to learn, tinker, or create. This infrastructure should be treated as educational public capital—like libraries, laboratories, and national broadband. And it should be designed at scale, with costs amortized across generations of innovators.

Because compute isn’t just about raw capacity. It is about creating a culture of experimentation.

Students must be allowed to make mistakes. To break things. To iterate. To test a model that fails spectacularly and then go back to the drawing board. Innovation is not a linear process—it is a messy, unpredictable dance between intuition and evidence.

You cannot simulate that with theoretical coursework alone. Students need the friction and the thrill of real compute. They need to feel the weight of training jobs, optimization choices, and the economics of scaling. They need to understand—through practice—what it takes to build responsibly.

And here is the point policymakers often miss: compute access is not merely a technical issue; it is an equity issue. Today, students from wealthier families or elite institutions have far more opportunities to engage meaningfully with AI. By institutionalizing compute access, governments can democratize innovation. They can turn AI from an exclusive domain into a civic resource.

The countries that dominate AI in the future will be those that empower their youngest minds today. Not by restricting models. Not by hoarding infrastructure. But by opening the doors to experimentation at scale.

If we want AI capacity, we must start at the roots. Give students the tools. Give them the compute. Then step back and let them invent the future—chaotically, ambitiously, and brilliantly.

That is how you build a nation capable of leading in AI.