How AI investments could undermine development goals in Africa

A new academic study has raised serious concerns about the rush to adopt artificial intelligence (AI) in resource-constrained African economies. Titled “Artificial Intelligence Investment in Resource-Constrained African Economies: Financial, Strategic, and Ethical Trade-Offs with Broader Implications”, the research was published in World, and presents a critical analysis of AI strategies in Ghana, Kenya, and Rwanda.

The paper, authored by Victor Frimpong, applies a political economy lens to evaluate AI investment strategies in the Global South, arguing that these initiatives often involve significant opportunity costs. It warns that high-profile AI projects, often driven by foreign partnerships and prestige, may divert funding from essential services such as water, education, healthcare, and rural infrastructure. The study further cautions that these investments risk intensifying inequality, fostering geopolitical dependency, and undermining digital sovereignty in countries where foundational development gaps remain severe.

How AI investment is reshaping budget priorities in Africa

According to the study, developing nations face mounting fiscal dilemmas when public funds are channeled into costly AI initiatives. Rwanda, for instance, launched a state-backed AI strategy in 2019 and received a $30 million loan from the African Development Bank to develop smart systems in hospitals. However, the research highlights that rural healthcare clinics in the country still lack basic water and sanitation infrastructure, which remains a more urgent and impactful investment need.

Similarly, Kenya committed $6 million to its 2025–2030 National AI Strategy to improve logistics and agricultural efficiency. Yet, in areas like Turkana County, over 80% of households still lack access to clean water, contributing to high rates of waterborne diseases and child mortality. In Nigeria, large AI data centers consume vast energy resources, equivalent to the daily consumption of 1,500 rural homes, while nearly half of the population lacks reliable electricity.

These examples illustrate the study’s central argument: in settings where budgets are limited and infrastructure is underdeveloped, prioritizing AI may inadvertently sacrifice investments in services with more direct social returns. The paper applies Lionel Robbins’ theory of opportunity cost to argue that such trade-offs are not merely financial decisions, but have deep ethical, developmental, and geopolitical implications.

Who benefits from AI in Africa and who gets left behind?

The study finds that the benefits of AI adoption often accrue to urban elites, exacerbating the digital divide and marginalizing rural and low-income communities. In countries like Kenya and Nigeria, global tech firms have rolled out services such as Facebook’s Free Basics, offering restricted access to internet content. While framed as inclusion initiatives, these programs limit informational autonomy and deepen dependence on proprietary platforms, what the paper describes as a form of “digital colonialism”.

AI-related job growth and educational programs have drawn talent away from essential public sectors. In Kenya and Ghana, increasing numbers of university students are enrolling in AI and data science programs, while enrollment in teaching and healthcare disciplines stagnates. The result, the study warns, is a gradual weakening of critical public institutions needed for equitable development.

Moreover, imported AI technologies, especially facial recognition, surveillance systems, and fintech platforms, are often deployed without adequate ethical or legal frameworks. Rwanda and Uganda, for example, have adopted smart city technologies developed by Huawei, raising concerns about data localization, privacy, and citizen surveillance. These systems, the study notes, are often incompatible with local norms and operate as opaque black boxes, limiting oversight and accountability.

The problem of “strategic technological dependency” is especially acute. Many African nations rely on foreign-built digital infrastructure, locking them into specific ecosystems that are expensive to exit. This dependence restricts the ability to build sovereign digital economies, limits regulatory control, and perpetuates reliance on foreign expertise and investment.

What policy solutions can guide equitable AI adoption?

In response to these concerns, the study proposes a four-part public policy framework for responsible AI investment in developing countries:

1. Sequencing and Readiness Assessment: Before pursuing AI, governments should assess digital infrastructure maturity, electricity reliability, and digital literacy levels. Investments should only proceed when foundational benchmarks are met.

2. Strategic Alignment with National Development Goals: Every AI initiative should be tied to Sustainable Development Goals (SDGs), undergo cost-benefit analysis including opportunity costs, and be assessed for social equity outcomes.

3. Ethical and Inclusive Governance: The paper calls for the creation of independent national AI ethics councils to ensure public accountability, conduct consultations with marginalized communities, and guard against undue foreign or corporate influence.

4. Capacity Building: At least a fixed percentage of AI investment budgets should be allocated to train civil servants, support local startups, and educate the broader public. This would reduce reliance on external expertise and help foster indigenous innovation ecosystems.

The model emphasizes that AI should be integrated gradually, ensuring foundational systems are in place before scaling advanced technologies.