Artificial intelligence is frequently described in terms of models, software and algorithms, yet its foundations remain deeply physical, anchored in materials, energy systems and global manufacturing networks. Within that structure, Africa occupies a position that is both critical and constrained. The continent accounts for around 70 percent of global cobalt mine production, primarily from the Democratic Republic of Congo, a material essential for battery systems that underpin energy storage for data centres and digital infrastructure. In parallel, countries such as Zimbabwe and Namibia are expanding lithium production, while rare earth deposits across Southern and East Africa are increasingly relevant to electronics and semiconductor components. These resources do not directly constitute artificial intelligence, but they sustain the energy and hardware systems without which large-scale compute cannot operate. Africa supplies many of the physical inputs that sustain artificial intelligence, but captures limited value in the layers where that intelligence is built, scaled and monetised.
Beyond the Democratic Republic of Congo, countries such as Zambia are expanding copper production critical for electrical systems, while Madagascar and Mozambique contribute graphite used in battery technologies. Yet most of these materials leave the continent in raw or minimally processed form. The value created through refining, component manufacturing and integration into semiconductor and energy systems is largely captured in Asia, Europe and North America, reinforcing a structural gap between resource ownership and economic return. This imbalance is reflected in the broader semiconductor industry, which exceeds $600 billion annually and remains concentrated in a small number of regions. Fabrication capacity is heavily centred in Taiwan, South Korea and the United States, where firms such as and dominate advanced chip production. Advanced-node fabrication is effectively concentrated in a handful of firms and geographies, reinforcing high barriers to entry for new regions and limiting participation in the most valuable segments of the supply chain.
Between raw material extraction and advanced semiconductor fabrication lies a middle layer of assembly, packaging and electronics manufacturing, where much of the incremental value is created. This layer remains underdeveloped across most African economies, with limited participation in component assembly or device manufacturing. The absence of this intermediate capability further compresses the continent’s role toward the lower end of the value chain, reinforcing dependency on external systems for transformation and integration.
Energy represents another layer where Africa’s role is both foundational and under-leveraged. Data centres, which form the backbone of AI compute, are energy-intensive assets, requiring continuous and stable power supply. Globally, data centres are estimated to consume between 1 to 2 percent of total electricity demand, a figure expected to rise as AI workloads scale. Africa, however, hosts well under 2 percent of global data-centre capacity, despite having significant renewable energy potential, particularly in solar and hydroelectric generation. Countries such as Morocco and Egypt are investing heavily in renewable energy capacity, while South Africa remains the continent’s most developed data-centre hub and Kenya has emerged as a regional digital infrastructure node in East Africa. Africa’s installed data-centre capacity remains small but is expanding, with major hubs emerging in Johannesburg, Nairobi, Lagos and Cairo, driven by hyperscale and colocation investments. Yet despite these advances, the scale and reliability required to support large AI compute clusters remain limited, forcing most high-intensity workloads to be processed outside the continent.
The data layer introduces a different form of participation. Africa’s digital population is expanding rapidly, with over 500 million internet users and mobile penetration exceeding 80 percent in many markets, generating large volumes of behavioural, transactional and communication data. Mobile money systems process enormous volumes of transactions, with global mobile money activity exceeding one billion transactions per day, and Sub-Saharan Africa accounting for the majority of active accounts and usage. However, as with physical resources, the infrastructure used to store, process and monetise this data is often controlled by external platforms. Cloud providers such as Amazon Web Services, Microsoft Azure and Google Cloud host a significant share of these workloads, meaning that data generated within African economies frequently flows through infrastructure that is not locally owned. This creates a recurring asymmetry: data is generated locally, processed on externally controlled infrastructure and monetised through platforms where value capture largely occurs outside the originating markets.
Labour and talent form another layer of the supply chain, though here the dynamics differ. Africa represents approximately 3 percent of global AI talent, yet countries such as Nigeria, Kenya, South Africa and Egypt have become important sources of engineering and technical expertise integrated into global technology ecosystems. Organisations such as Andela, which has trained over 110,000 developers, have enabled distributed work models in which African engineers contribute to global products while operating from local markets. This positions the continent as a provider of digital labour within the AI supply chain, but limits the extent to which value generated by that labour is retained locally, reinforcing a pattern in which participation does not equate to control.
These layers—materials, manufacturing, energy, data and labour—collectively define Africa’s position in the global AI supply chain. In each case, the continent plays a role that is upstream or intermediate, but rarely downstream where the highest value is captured. Critical minerals are extracted locally but processed elsewhere. Data is generated locally but monetised through external platforms. Talent is developed locally but integrated into global firms. Compute infrastructure, the core of modern AI systems, remains largely external. Africa’s position in the AI supply chain is therefore not marginal, but structurally imbalanced—embedded in the system’s foundations, yet distant from its centres of control.
The implications of this structure are both economic and strategic. As artificial intelligence becomes more central to global productivity and competitiveness, control over supply chains—whether physical or digital—translates into geopolitical influence. Countries and regions that dominate semiconductor manufacturing, cloud infrastructure and model development are able to shape not only technological standards but also economic outcomes. Africa’s current position limits its ability to exert similar influence, despite its importance as a source of inputs.
There are, however, emerging shifts that could alter this trajectory. Investments in data-centre infrastructure are increasing, with new facilities being developed in markets such as South Africa, Kenya and Nigeria. Hyperscale cloud providers are expanding regional presence, and local firms are beginning to explore opportunities in edge computing and distributed infrastructure. At the same time, growing interest in critical minerals has led to discussions around local beneficiation—processing resources within the continent rather than exporting them in raw form. These developments suggest the possibility of moving further along the value chain, though significant challenges remain in capital, technology and policy coordination.
The central question is whether Africa can reposition itself within the AI supply chain from a provider of inputs to a participant in value creation. This does not require full replication of global manufacturing ecosystems, but it does require strategic movement into higher-value segments—whether in data infrastructure, specialised hardware assembly, applied AI systems or regional platforms. Without such shifts, the existing pattern is likely to persist, with Africa supplying the foundations of the AI economy while capturing only a limited share of its returns.
Artificial intelligence is not only a technological transformation; it is a reconfiguration of global supply chains. Within that reconfiguration, Africa’s role is already defined, but not fixed. The continent’s resources, data and labour position it as an essential part of the system. The challenge lies in converting that position into sustained economic leverage, ensuring that participation in the AI economy translates into ownership, capability and long-term value. Africa is structurally embedded in the inputs of the AI economy, but remains underrepresented in the layers where control, pricing power and long-term value are set.
