Take the Train: Africa at the Crossroad of Modern AI

TL;DR

This paper highlights Africa's infrastructural and policy challenges in adopting modern AI, emphasizing the need for digital foundations and introducing the Africa AI Compute Tracker to monitor HPC resources.

Contribution

It introduces the Africa AI Compute Tracker, an open-source tool to monitor HPC availability, and emphasizes the importance of digital enablers for sustainable AI adoption in Africa.

Findings

Africa faces significant infrastructural barriers to AI adoption.

The Africa AI Compute Tracker is the first open-source map of HPC resources in Africa.

Sustainable AI growth in Africa depends on balanced access to compute, data, and energy.

Abstract

Africa's participation in modern AI development is constrained by severe infrastructural and policy gaps. Important barriers include limited access to high-performance computing (HPC), restricted cloud access due to payment system mismatches, volatile exchange rates, and strict data sovereignty laws that fragment regional collaboration between African Union (AU) member states. Although initiatives such as Cassava AI's network of AI factories to be deployed across the continent signal the growing interest in adopting AI in Africa, these projects remain very targeted, while continental adoption still requires better coordination between African stakeholders. Drawing on official declarations on AI adoption across the continent, this paper offers both qualitative and quantitative evidence that sustainable AI adoption requires robust digital foundations through balanced access to compute, data, and the energy that makes it possible. We refer to these foundations as the "right enablers", considering them as crucial components for success within the current context of the global AI race. We also introduce the \textit{Africa AI Compute Tracker (ACT)}, an interactive map to monitor the availability of AI-ready HPC systems throughout the continent. This tool represents the first open-source effort to consolidate data on Africa's evolving HPC landscape, and aims to encourage more transparency from local AI stakeholders while facilitating broader access for AI developers. The work presented in this paper underscores the urgency of tangible actions aimed at closing the AI divide and allowing Africa to actively shape its AI future.