Artificial Intelligence is rapidly becoming the most transformative technology of the twenty-first century. Much like electricity fueled industrialization and the internet accelerated globalization, AI is reshaping economies, governments, and societies on a global scale.
The benefits are undeniable. AI can improve healthcare outcomes, enhance educational opportunities, increase agricultural productivity, and make public services more efficient. For developing countries, AI may even provide opportunities to leapfrog traditional stages of economic development.
Yet alongside these opportunities comes a fundamental challenge. As AI systems become increasingly dependent on vast quantities of data, powerful computing infrastructure, and sophisticated foundation models, economic and political power is becoming concentrated in the hands of a small number of countries and technology companies.
The central question is therefore no longer whether AI will transform society. The real question is who will control the resources that make AI possible and whether the benefits of this transformation will be distributed equitably.
Few individuals have contributed more to modern artificial intelligence than Geoffrey Hinton.
Widely known as the "Godfather of AI," Hinton's pioneering work on artificial neural networks laid the foundation for the deep learning systems that power today's generative AI technologies. In recognition of these contributions, he was awarded the 2024 Nobel Prize in Physics.
What makes Hinton particularly influential, however, is that he has also become one of the strongest advocates for responsible AI governance. Having helped create the technology, he has repeatedly warned about the societal risks that may accompany increasingly powerful AI systems.
His message is clear: innovation alone is not enough. Humanity must develop institutions capable of governing AI responsibly.
This warning is particularly relevant for developing countries, where the consequences of technological dependence may be especially significant.
Historically, global power has been associated with control over territory, natural resources, industrial production, and military strength.
Today, a new form of power is emerging.
Countries and corporations that control data, advanced semiconductors, cloud infrastructure, and frontier AI models increasingly shape the global digital economy.
This phenomenon is often described as AI imperialism.
Unlike traditional imperialism, AI imperialism does not require physical occupation or political domination. Instead, dependence emerges through digital ecosystems.
A country may maintain political independence while relying heavily on foreign AI models, cloud services, software platforms, and data infrastructures.
The result is a subtle but powerful form of dependency in which critical technological capabilities remain concentrated elsewhere.
At the heart of AI imperialism lies a more fundamental issue: data sovereignty.
Data sovereignty refers to the ability of individuals, organizations, and nations to control how their data is collected, stored, processed, transferred, and used.
In the AI economy, data is not merely information.
It is a strategic resource.
Just as oil fueled industrial growth in the twentieth century, data fuels AI development in the twenty-first.
Countries that generate large amounts of valuable data but lack the capacity to process and utilize it may find themselves exporting raw data while importing expensive AI services built upon that same information.
This pattern resembles historical resource extraction models.
Raw materials leave developing economies.
Value-added products return at higher prices.
The same dynamic may emerge with data.
Developing countries increasingly face a difficult question:
Data sovereignty therefore becomes more than a privacy issue.
It becomes an issue of economic development, national competitiveness, and digital self-determination.
Without meaningful control over data, meaningful control over AI may become impossible.
One of the most important debates of the coming decades concerns ownership and control of humanoid robots.
At first glance, ownership appears straightforward.
The buyer purchases the robot and determines how it is used.
A factory owner, hospital administrator, or logistics company decides where the robot operates and what tasks it performs.
From this perspective, humanoid robots are simply advanced tools.
However, a deeper examination suggests a more complicated reality.
The physical robot may belong to the buyer, but the intelligence that powers it may remain under the control of technology companies.
Software updates, foundation models, cloud-based learning systems, and safety protocols are often managed remotely by the organizations that developed them.
Consequently, ownership of hardware may not imply ownership of intelligence.
The purchaser controls the body, but the technology provider controls the brain.
This distinction has profound implications for economic independence, competition policy, and national sovereignty.
As humanoid robots become increasingly integrated into industry and everyday life, the future balance of power may depend on four actors:
Understanding how these stakeholders interact will be one of the central governance challenges of the twenty-first century.
Perhaps the most pressing concern from a development perspective is the possibility that AI could widen global inequality.
Developed economies possess several advantages:
Many developing countries lack these resources.
As AI increasingly drives productivity growth, countries without the necessary infrastructure may find themselves excluded from the most valuable segments of the global economy.
This could create a cycle of cumulative advantage.
Countries with strong AI capabilities become more productive, attract additional investment, generate more data, and develop even better AI systems.
Meanwhile, countries with limited capabilities struggle to catch up.
The consequences extend beyond economics.
Educational opportunities, healthcare quality, public administration efficiency, and labor market outcomes may increasingly depend on access to AI technologies.
Without deliberate intervention, AI could reinforce existing inequalities both within and between nations.
Nevertheless, the future is not predetermined.
AI can also create opportunities for leapfrog development. Just as mobile phones allowed many countries to bypass traditional telecommunications infrastructure, AI may enable developing nations to accelerate progress in education, healthcare, agriculture, and financial inclusion.
The outcome will depend largely on governance and investment decisions made today.
Organizations such as the World Bank have a unique responsibility in shaping an inclusive AI future.
Traditionally, development institutions focused on roads, ports, electricity, and water systems.
Today, digital infrastructure must be viewed as equally important.
The World Bank can contribute in several critical areas.
Reliable internet connectivity, cloud computing resources, and digital public infrastructure are essential foundations for AI adoption.
Countries require skilled workers, researchers, policymakers, and regulators capable of understanding and managing AI technologies.
Governments need expertise to evaluate AI systems, negotiate with technology providers, and develop effective regulatory frameworks.
Countries need the ability to establish rules governing the collection, processing, sharing, and cross-border transfer of data while maintaining openness to innovation and international cooperation.
Development institutions can help ensure that small businesses, rural communities, and underserved populations benefit from AI-driven growth.
The World Bank can facilitate dialogue among governments, private-sector actors, and civil society organizations to establish common principles for responsible AI development.
In many respects, promoting equitable access to AI may become as important as providing access to electricity was in the twentieth century.
Privacy is often discussed as an individual right.
In the AI era, it is also a foundation for trust.
Citizens must know how their personal information is collected, processed, and used.
Governments must establish transparent rules regarding accountability, explainability, and redress mechanisms.
Strong AI governance should incorporate several core principles:
Privacy by Design emphasizes that privacy protections should be integrated into technologies from the earliest stages of development rather than added after deployment.
Ultimately, trustworthy AI requires trustworthy institutions.
The objective should not be to slow innovation but to ensure that innovation serves human interests and democratic values.
The future of AI should not be framed as a choice between innovation and regulation.
Both are necessary.
Countries need access to advanced technologies, but they also need the capacity to govern them.
Data sovereignty should not be interpreted as digital protectionism.
Rather, it should be understood as the ability of societies to make informed decisions regarding how data is used and who benefits from its value.
The most successful countries will likely be those that combine:
A truly inclusive AI future is one in which technological benefits are broadly distributed, opportunities are accessible to all, and innovation strengthens rather than weakens social cohesion.
Geoffrey Hinton's career captures the paradox of the AI age.
The scientist whose discoveries helped create modern artificial intelligence is also among those urging caution about its future.
His warning reminds us that the greatest challenge is not technological.
It is institutional.
Artificial intelligence will undoubtedly transform the global economy. The question is whether it will create a more inclusive world or a more unequal one.
The answer will depend on how effectively governments, businesses, and international organizations address the interconnected challenges of AI governance, privacy, and data sovereignty.
In the twenty-first century, sovereignty is no longer defined solely by borders, territory, or military power.
It increasingly depends on who controls data, who controls intelligence, and who ultimately benefits from the value they create.
Ensuring that this value is shared broadly rather than concentrated narrowly may become one of the defining development challenges of our time.
The future of AI should therefore be measured not only by its intelligence, but by its contribution to human dignity, equality, freedom, and shared prosperity.