AI Policy, Infrastructure, and Neo-Colonial Dynamics in Africa

Vusumuzi Vusumuzi; Notice Pasipamire

Authors

Vusumuzi Vusumuzi National University of Science and Technology Author
Notice Pasipamire National University of Science and Technology Author

Keywords:

Artificial Intelligence, Digital Infrastructure, Data Sovereignty, Neo-Colonialism, Brain Drain, African Languages in AI

Abstract

Many African nations are increasingly formulating national artificial intelligence (AI) policies. While these initiatives signal positive progress, their effectiveness is constrained by weak infrastructure and a fragmented digital economy. This paper situates Africa’s AI policy responses within the global scramble for AI governance, showing how technological determinism, digital colonialism and entrenched social inequalities shape them. Current AI policy approaches often prioritise regulation and licensing as revenue-generating mechanisms for African governments. This imbalance risks stifling indigenous AI development, positioning Africa within neocolonial dynamics in which short-term fiscal priorities overshadow long-term investment in infrastructure, skills, and transformative innovation. This study adopted a conceptual and interpretive methodology, designed to analyse the interplay between AI policy, infrastructure, and neo‑colonial dynamics in Africa. Guided by the dependency theory, neo-colonialism, digital sovereignty, and innovation systems, this study highlights Africa’s marginal position in the global AI ecosystem. The paradox is stark: Africa supplies rare earths and critical minerals essential for AI development and data centres, yet it remains economically marginalised and benefits disproportionately from this contribution. Most of Africa’s digital networks and routes continue to reflect colonial legacies, with limited inter-African communication. Although most African AI policies emphasise data sovereignty and ethics, fewer than 1% of global data centres are in Africa. Persistent challenges include siloed digital systems, limited investment in data science education, and brain drain, with Western AI systems benefiting while African AI systems remain underdeveloped. Cultural and linguistic exclusion further compounds these dynamics, as African languages and identities are underrepresented in AI ecosystems. Africa requires investments in infrastructure, regional cooperation, skills development, and policy frameworks. Recommendations include interoperable infrastructure, regional data centres, public-private partnerships, and talent retention.

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