A New Engine of Inequality
Artificial intelligence is not just a new tool; it is rapidly becoming the backbone of economic power and political influence. As the biggest tech firms and richest states pour money into advanced models, data centers and specialized chips, a clear pattern is emerging; AI progress is clustering where money, talent and infrastructure already exist. That cluster, dominated by the United States, China and a few wealthy European states, risks turning AI into another engine of global inequality, leaving large parts of the developing world dependent, marginalised and slower to benefit from the technology’s promise.
The Power of Scale
At the heart of the problem is scale. Cutting-edge AI requires enormous computing power, specialised processors and vast, well-curated datasets. The so-called “training and inference economy” favours organisations that can afford hundreds of millions, even billions, of dollars of cloud compute and custom hardware. These resources are concentrated in hyperscale cloud providers and a handful of national champions that have the capital and policy backing to build massive data centres. The result is a two-speed world. Countries and firms with access to large-scale compute accelerate quickly; others can only rent access, often at a cost that makes homegrown innovation difficult.
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The Cloud Dependency Trap
Cloud dominance compounds the effect. The global cloud market is largely controlled by a few players whose services now include pre-trained models, developer tools and managed AI pipelines. For many governments and companies in the Global South, it is easier and cheaper to consume AI through these platforms than to build capacity locally. That consumption creates dependency: intellectual property, user data and the high-margin services that value-capture flows back to the providers. Over time, this dynamic can hollow out local digital industries, keeping much of the value chain, from model ownership to monetisation, outside developing economies.
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Policy and Power in the AI Race
Geopolitics and industrial policy have hardened these imbalances. The United States has private sector giants and deep venture capital networks; China combines state support with enormous domestic markets and fast product rollouts; the European Union is prioritising regulation and standards while also trying to nurture its own AI industrial base. China’s recent large-scale investment funds and corporate pledges have bulked up national AI capacity, while Western cloud and chip investments have also grown to levels few developing countries can match. These policy choices matter: they shape who gets to set technical standards, who controls supply chains for chips and servers, and who writes the rules for data flows and digital services.
The Developing World’s Dilemma
For Pakistan and countries like it, the consequences are mixed. On one hand, access to affordable cloud services and AI APIs provides a path to build applications quickly, from health diagnostics to crop forecasting. On the other hand, without sustained investment in local infrastructure, skills and data governance, most high-value work, model training, ownership of IP, and the lucrative business of fine-tuning and deployment at scale, will remain elsewhere. That keeps countries in a role that is closer to consumer than producer, capturing limited revenue while paying for services that underpin their own digital transformation.
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Data Colonialism and the Brain Drain
The danger is not purely economic. There is a risk of a new form of “data colonialism,” where user data from poorer countries fuels models that are owned and monetised in richer ones. When model outputs reflect values encoded in datasets collected elsewhere, the services can be less relevant or even harmful in local contexts. Moreover, the migration of skilled engineers and researchers to well-funded labs abroad, a brain drain accelerated by lucrative offers and modern research environments, weakens the domestic talent pool that could otherwise build homegrown solutions. These are structural problems that simple access to cloud APIs cannot fix.
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Narrowing the Divide
Yet the picture is not hopeless. International institutions and development banks are aware of the stakes, and there are concrete interventions that can narrow the divide. Financing for data centres, targeted grants for compute in universities, regional centres of excellence, and partnerships that prioritise technology transfer can all help. The World Bank and other multilaterals have reinforced that digital progress is uneven and highlighted the importance of policies that encourage adoption while protecting public interest. But such programmes must be faster, better resourced and aligned with national industrial strategies if they are to counterbalance market forces.
The Role of the Private Sector
Private sector behaviour matters too. Tech companies can take more active steps to share models, lower prices for compute in developing markets, and support local research with funding and knowledge transfers. Open models and transparent licensing make it easier for local innovators to iterate without facing prohibitive costs or legal uncertainty. But voluntary measures are no substitute for public policy: competition authorities, trade policy and industrial incentives will determine whether local ecosystems can capture more of the AI value chain.
Pakistan’s Path Forward
For policymakers in Pakistan, the immediate priority should be pragmatic: invest in digital infrastructure that supports AI work (reliable power, fibre, edge compute), upgrade higher education with AI-focused curricula and internships, and design data governance rules that protect citizens without strangling innovation. At the same time, Pakistan should pursue partnerships that build capacity, not dependency, for instance, co-development deals with cloud providers or regional consortia that pool resources for shared compute and data stewardship. Small and medium enterprises need incentives to adopt AI safely, while regulators must guard against foreign dominance that undermines local competition.
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Conclusion: The Moral Imperative
The moral and political imperative is clear. Artificial intelligence will influence who wins in the 21st-century economy. If access to compute, capital and skilled people continues to be the preserve of a handful of countries and companies, the benefits of AI will be uneven: faster growth, new industries and better public services for some; increased dependency and lost opportunities for others. That outcome is not inevitable. With deliberate policy, targeted investment and international cooperation, countries like Pakistan can move from being passive consumers to active participants in shaping AI’s future. But time is short: the longer the delay, the wider the divide will grow, and the harder it will be to close.
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