Ai’s quiet drift toward structural inequality

Editorial Team SDG10
14 minutes ago
4 min read

A widening technological divide threatens to reshape development trajectories worldwide

As artificial intelligence accelerates across economies, its promise is celebrated as broadly as its risks are underestimated. One of the most pressing concerns is the possibility that AI-driven inequality becomes structural, embedding itself into the foundations of economic growth, public services and social mobility. In a world already marked by disparities in infrastructure, skills and governance, the arrival of advanced automation introduces a new fault line. Addressing it is essential for global society and fair sustainability, especially in light of the SDG commitment to reduce inequality within and among countries.

Uneven readiness could cement a new era of divergence

Countries and regions begin the AI transition from vastly different starting points. Access to fast connectivity, stable electricity, digital hardware and computing capacity remains deeply uneven. In lower income or rural areas, these deficits risk becoming self-reinforcing. Where digital foundations are weak, AI systems cannot be deployed widely enough to support public services, education or small-business productivity. Where they are strong, AI becomes a multiplier of advantage.

The disparity in digital ecosystem strength, already measured in some regions to vary by more than two hundredfold, illustrates the scale of the challenge. Such gaps mean that only better resourced economies may benefit from AI’s productivity gains, while others struggle to build the institutional and regulatory capacities required to use these tools responsibly.

Skills, governance and the human dimension of inequality

Alongside infrastructure, the availability of skilled people will be decisive. AI demands data-literate teachers, engineers, regulators and public-sector specialists. Without targeted investment in education and lifelong learning, some countries risk long-term exclusion from emerging industries. Within countries, the divide between well-connected urban centres and underserved rural communities may deepen, especially where young people lack access to training.

Institutional strength matters just as much. Effective AI governance depends on transparent regulation, robust oversight, ethical standards and public accountability. Weak institutions can lead to the misapplication of algorithms and to biased data systems that overlook marginalised groups. These patterns may quietly entrench social disadvantage, making inequality harder to reverse.

Economic polarisation and job displacement

AI’s influence on labour markets is uneven. Automation threatens roles that depend on routine tasks, and many developing economies rely heavily on such sectors. Without coordinated strategies for retraining and economic diversification, job losses could widen socio-economic divides. Meanwhile, higher income countries capable of developing or acquiring powerful AI systems may concentrate new value in advanced industries, intensifying global divergence.

A further complication is the emergence of data-rich and data-poor environments. Organisations and countries with abundant, high-quality data will build more accurate models and deploy more competitive services. Those without such resources risk becoming peripheral to the digital economy.

Asia Pacific as a bellwether for global fractures

Nowhere are the stakes more visible than in Asia Pacific, where digital readiness spans from global leaders in technology to countries still expanding basic connectivity. Even within middle-income states, contrasts between metropolitan hubs and rural provinces are stark. Without proactive policies, this heterogeneity could produce new development corridors that benefit only a fraction of the population.

For rapidly transforming economies such as Thailand and its neighbours, inclusive regulation, public-sector capacity building and investment in rural infrastructure will shape whether AI reduces or reinforces existing divides.

Social and geopolitical implications beyond income

The consequences of unequal AI access extend far beyond economics. If public services such as healthcare, disaster response or education rely increasingly on AI, communities without access may experience deteriorating outcomes. This, in turn, could increase pressure on migration flows and intensify regional instability.

Concentrating AI capabilities in a small number of countries or corporations also risks reshaping global power balances. When decision-making tools and data infrastructures sit predominantly in wealthier states, the influence gap widens, challenging fairness and cooperation in international development. Loss of trust may follow if marginalised groups find themselves misrepresented or excluded by systems built without their data or input.

What an inclusive AI trajectory requires

A fair technological transition will depend on deliberate choices. Investments in digital infrastructure must reach remote and disadvantaged areas. Education and skills programmes need to prepare young people not only to use AI but to shape and govern it. Transparent regulation is essential to protect privacy, ensure equitable access and prevent algorithmic harm.

Governments can harness AI for inclusive public-service delivery, provided that deployment is matched with safeguards and community engagement. Technological cooperation, knowledge sharing and open research can further reduce disparities in access to advanced models.

Above all, policymakers must view AI not as a neutral instrument but as a system whose benefits and harms depend heavily on context. Without timely action, the risk is that inequality becomes embedded in the architecture of the digital age.