When OpenAI launched ChatGPT to the public, generative artificial intelligence quickly became one of the fastest-growing consumer technologies in history.
Like many major technological changes, its arrival triggered widespread worries, with one of the biggest fears being that AI would replace millions of jobs.
However, the impact of AI has taken a different direction. Instead of an immediate workforce collapse, the technology has highlighted a bigger reality in which intelligence is becoming one of the world’s most valuable resources.
Developing advanced AI systems requires massive computing power, billions of dollars in investment, large-scale data centres, huge energy supplies and access to some of the most advanced semiconductor technologies available today.
As a result, artificial intelligence is no longer viewed only as a software innovation. It is becoming a matter of economic competitiveness, national security and technological independence.
The Hardware and Software Bottleneck
The global AI industry depends on a highly concentrated supply chain. Many of the world’s most advanced AI chips, including NVIDIA Blackwell GPU processors, are designed by a small number of companies and manufactured largely by Taiwan Semiconductor Manufacturing Company (TSMC).
The same semiconductor ecosystem also supports several leading consumer technology products, including devices produced by Apple.
This concentration has turned advanced chip manufacturing into a strategic asset. It is one reason the United States has continued to introduce restrictions on exports of advanced chips and semiconductor manufacturing equipment, particularly in relation to strategic competition with China.
The restrictions reflect a growing belief among governments that control over advanced computing infrastructure will play a major role in determining the future direction of artificial intelligence.
But the competition is not only about hardware. The software and infrastructure running on these chips are equally important.
Governments are increasingly examining their dependence on foreign cloud providers such as Microsoft, Google and Amazon Web Services, as concerns grow over long-term control of critical digital infrastructure.
If a country depends entirely on external providers for its AI capabilities, its ability to develop and manage strategic technologies may be limited.
One recent example highlighting these concerns is the reported suspension of access to some advanced AI models developed by Anthropic. The move reflected growing government interest in controlling access to powerful AI systems over concerns about potential misuse, including attempts to bypass safeguards and identify software vulnerabilities.
Whether such restrictions become temporary measures or a permanent feature of the AI industry, they point to a broader shift away from a completely open AI ecosystem.
The world may be moving towards a period of “AI nationalism”, where access to the most powerful AI systems depends increasingly on geography, regulation and international alliances.
Europe’s Push for AI Independence
For years, Europe’s approach to artificial intelligence focused heavily on regulation, with policymakers prioritising safety, transparency and accountability.
However, discussions around Europe’s technology strategy have increasingly shifted towards building domestic AI capabilities.
European leaders and companies are now placing greater emphasis on developing local AI infrastructure, expanding data centre capacity and supporting homegrown companies such as France-based Mistral AI.
The objective is no longer only to regulate AI but also to reduce dependence on foreign technology providers and strengthen Europe’s ability to build and control its own AI systems.
Africa’s AI Infrastructure Challenge
For Africa, the growing competition around AI infrastructure presents a significant challenge.
The continent remains heavily dependent on AI technologies developed elsewhere, often using foreign datasets and infrastructure. As access to advanced AI systems becomes more strategic, African countries risk becoming consumers of technology rather than major contributors to its development.
A major concern is the continent’s limited digital infrastructure. Despite accounting for a significant share of the world’s population, Africa still has a relatively small portion of global data centre capacity.
This limits computing power, reduces control over sensitive data and makes it harder to develop AI systems designed around local needs and realities.
The challenge is largely linked to years of limited investment in digital infrastructure, electricity supply and advanced technology development.
However, some African countries are beginning to place greater focus on AI capacity building.
Nigeria, for example, has increased efforts around digital infrastructure, local data management and policies aimed at strengthening technology development within the country.
These steps represent progress, but there are still significant gaps.
Building advanced semiconductor manufacturing capabilities requires decades of investment, reliable electricity, specialised expertise and strong industrial supply chains. For many African nations, expanding data centre capacity, improving connectivity and strengthening power infrastructure remain more immediate priorities.
The Future of AI Power
Artificial intelligence is becoming more valuable because it depends on increasingly scarce physical resources.
The development of advanced AI requires powerful chips, large-scale data centres, enormous energy consumption and billions of dollars in investment. Only a limited number of countries and companies currently have the capacity to provide these resources at scale.
At the same time, AI is becoming more strategically important because of its potential applications in cybersecurity, scientific research, software development, business automation and national defence.
The countries that control the strongest AI infrastructure will likely have significant influence over the digital economy and the future of automated work.
This is why the global AI race is no longer only about creating smarter models. It is increasingly about controlling the infrastructure, resources and systems that make those models possible.
