Many people compare artificial intelligence to oil. Yes, the oil analogy is powerful, but it breaks down at a certain point. The logic of oil was simple: it was either underground or it wasn't. Data doesn't work that way. Data is produced everywhere. In Nigeria, Indonesia, Brazil. The real issue isn't who produces it, but who processes it and who monetizes it. Therefore, the real oil of the 21st century is not raw data, but processed, organized, ready-to-use data. And those factories are currently in very few hands.

The real danger here isn't that countries stay out of AI. Staying out is visible, it elicits reactions, it generates policies. The real danger is much quieter: joining the system and remaining at the bottom. We saw this in globalization; many countries entered the system, but the most valuable links always remained in other hands. This will be even harsher with AI because this time, machines are also replacing human labor.

What do countries that adopt AI gain?

Look at the US. Their strategy isn't just about developing models, but about controlling the entire chain. #OpenAI, Anthropic, $GOOGL, $META – these aren't just individual companies; they represent the visible face of the US industrial policy in the age of AI. The AI ​​executive orders signed in the early days of the Trump administration clarified this picture: reduce regulation, accelerate development, maintain global leadership. The Stargate project announced a $500 billion infrastructure investment. This figure is no coincidence; it's a move to secure a foothold.

Countries that adopt AI early and deeply gain several tangible advantages. First, the productivity gap: more output with the same workforce. This difference may seem small, but it works like compound interest; look at the gap between two countries in five or ten years. Second, talent attraction: good engineers, researchers, and entrepreneurs go to places with strong ecosystems. The US has been running this cycle for decades, and AI is accelerating it even further. Third, rule-making power: whoever develops the technology also sets the standard. GDPR was a defensive move by Europe. The US's AI standards, however, are an offensive game, allowing its own companies to operate freely on a global scale.

China sees this picture and is pursuing a different path. Its own model ecosystem, its own chip development efforts, its own data governance rules. Deepseek's emergence was significant in this respect, showing that it's possible to circumvent the US's computing superiority to some extent. This competition is not only technological but also geopolitical. And the space is increasingly shrinking for countries in the middle.

What are countries losing that cannot adopt artificial intelligence?

The observation that "the cheap labor advantage is eroding" is true, but it's only half the story. What's really happening is this: when that advantage erodes, there's very little time to replace it with something else. South Korea and Taiwan made this transition over decades, with conscious policies. That window is now closing.

As production automation advances, the number of companies looking for "cheap labor" to build factories decreases. This means that the traditional development path, first textiles, then assembly, then more complex production, may not work. Bangladesh, Ethiopia, Vietnam are moving or trying to move along this path. But the pace of automation in the US is shortening this path.

A similar picture exists in agriculture. AI-powered agricultural technologies empower large-scale farmers with access to capital through precision irrigation, disease detection, and yield estimation. If small farmers lack access to these tools, the productivity gap widens, making them unable to compete. This is a matter of inequality within a country, but the same dynamic operates between countries.

The most silent loss is brain drain. Talented individuals with access to AI tools, good internet infrastructure, and the ability to work remotely for global companies no longer have to physically move, but they are transferring the economic value they could create in that country elsewhere. This is an invisible but cumulative loss. The question of sovereignty versus access is not as clear-cut as it seems. Most countries choose access, and rightly so. But the price of access is usually this: decisions are made from another center. This isn't a new colonialism, but a new dependency. And the most dangerous aspect of this dependency is its invisibility. You see a port, you see a road. You can't see an API dependency, a model layer.

The US is aware of this dependency and uses it as a tool. Chip restrictions, model export regulations, control over cloud infrastructure – these are all economic tools, but they have geopolitical consequences. Which country has access to which model, which computing power, which data is becoming an increasingly political question.

I think the real question isn't "How do we get involved in AI?" but rather "Which link in the value chain do we want to be in, and how much time do we have to get there?"

Currently, power is concentrated in two areas: computing power and the modeling layer. Both are very expensive, require scaling, and are first-in, first-out areas that gain a lasting advantage. Competing here isn't realistic for most countries. But in other links of the chain, sector-specific data, local language models, application development, and rule-making have a real chance of establishing a foothold.

The real scarcity isn't in raw materials, but in the capacity to understand and transform them. But something needs to be added: the scarcity is also in being able to do this according to your own rules. Acquiring that capacity by relying on someone else's model, someone else's system is one thing; internalizing it and being able to reproduce it is another. Without the latter, the former always remains fragile, and this fragility isn't just economic. If a country's artificial intelligence infrastructure is entirely built abroad, that infrastructure could one day be shut down. This isn't an exaggerated scenario; we've seen in recent years how technology export restrictions have become a weapon. Data and model dependency further deepens this risk.