Will AI Bridge the Development Gap or Widen It?

Artificial Intelligence (AI) is rapidly reshaping the global economy, with predictions suggesting it could contribute between $15 and $20 trillion globally within the next decade. While the transformative potential of AI across sectors like healthcare, agriculture, education, and finance is widely acknowledged, its uneven distribution raises critical questions regarding whether AI will ultimately accelerate self-reliance or deepen existing inequalities, especially in developing countries.

AI as a Catalyst for Development and Economic Growth

Proponents argue that AI technologies offer developing economies opportunities to leapfrog traditional infrastructural hurdles, enabling significant economic and social advancements. For instance, AI-powered healthcare solutions have begun addressing public health challenges in Africa, including predictive diagnostics for infectious diseases, personalized treatment applications, and improvements in hospital safety standards. In agriculture, AI-driven advisory services have boosted smallholder farmers’ productivity and resilience to climate change, directly contributing to food security. Likewise, AI-based educational platforms allow for tailored, scalable educational services, filling crucial gaps where traditional school infrastructure or qualified teachers are scarce. Financial inclusion also benefits from AI-driven credit scoring and banking services, enabling previously unbanked populations to access essential economic resources.

Structural Barriers to AI Adoption and Innovation

However, despite these promising developments, significant structural barriers limit equitable AI adoption in the Global South. One core issue is inadequate infrastructure, particularly the absence of reliable energy supply, high-performance computing resources, and stable internet connectivity. AI’s computational demands are considerable; building and operating the required data centers can consume as much energy as a small city. For many developing nations already experiencing energy deficits, meeting these requirements is an enormous challenge.

Another significant obstacle is the stark imbalance in global AI investment and research. Africa, for example, currently receives less than 1% of total global AI investment. The overwhelming majority of AI datasets, research findings, and algorithm development occurs in advanced economies, predominantly in North America, Europe, and East Asia. As a result, existing AI models often lack relevance to the specific linguistic, cultural, and societal contexts of developing countries, reinforcing rather than bridging digital divides. Without targeted investments and localized data infrastructures, AI applications risk perpetuating biases and entrenching technological dependency rather than fostering genuine innovation and local entrepreneurship.

The regulatory landscape for AI governance is similarly challenging. Many developing countries have been slow to implement comprehensive AI strategies or coherent regulatory frameworks, leaving them vulnerable to potential abuses of privacy, data security risks, and algorithmic biases. The absence of clear regulatory guidelines hampers innovation, creates uncertainty for potential investors, and slows the integration of AI technologies into broader economic strategies. Additionally, inadequate regulatory structures may facilitate misuse of AI technologies in contexts such as surveillance or authoritarian governance, raising concerns about ethical use and accountability.

Geopolitical Considerations and AI Governance

Geopolitical competition further complicates the picture, particularly between leading AI powers such as the United States and China. Recent advances, including the development and rapid global dissemination of sophisticated open-source AI models, highlight ongoing tensions around AI accessibility, intellectual property, and export controls. Developing nations find themselves navigating between competing AI ecosystems, potentially limiting their ability to freely adopt or adapt emerging technologies. This competitive environment underscores the need for multilateral collaboration and coordinated governance frameworks, providing developing nations with collective leverage rather than forcing them to individually navigate geopolitical pressures.

Policy and Investment Strategies for Bridging the AI Divide

Addressing these barriers requires deliberate and coordinated action across multiple fronts. Experts suggest policy measures including regulatory sandboxes—environments where innovators and policymakers collaboratively experiment with AI regulation—allowing innovation to flourish within clearly defined ethical and safety boundaries. Investment in localized data infrastructure, targeted training programs, and capacity-building initiatives are also crucial. Such programs would empower developing countries to become active producers and innovators in AI, rather than mere consumers of externally designed systems.

Conclusion

Ultimately, AI’s role in global development hinges on addressing foundational inequities in infrastructure, investment, research, and regulatory capacity. If managed strategically, AI could become a powerful instrument for economic and social advancement in the Global South, significantly enhancing self-reliance. Conversely, without addressing these systemic challenges, AI risks deepening global inequalities, consolidating existing power dynamics, and leaving many developing nations further behind in an increasingly digitalized world.

Sources

• CSIS. 2025. Artificial Intelligence and Global Development: Accelerating Inequality or Self-Reliance? Center for Strategic and International Studies. March 14.

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