Take the Train: Africa at the Crossroad of Modern AI
C\'edric Manouan, Miquilina Anagbah, N'guessan Yves-Roland Douha, Jo\~ao Barros

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,…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsICT in Developing Communities · Scientific Computing and Data Management · Ethics and Social Impacts of AI
