Nigeria minister calls for shared computing capacity to support AI development
At the BUILD 2026 conference, the communications minister cited power, connectivity, cloud access and advanced chips as constraints on local AI growth.
AI-generated symbolic illustration. Symbolic compute infrastructure; no actual facility, branded design or deployed autonomous system is depicted. · Business / Haber Paper
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Nigeria’s Minister of Communications, Innovation and Digital Economy, Bosun Tijani, called for shared computing infrastructure and stronger regional cooperation to accelerate artificial-intelligence development. Speaking through the National Centre for Artificial Intelligence and Robotics director at the BUILD 2026 conference in Abuja on 21 October, he said electricity, broadband, cloud capacity and access to advanced chips were major constraints.
Tijani pointed to plans for a Nigeria–Togo data park and expanded fibre connectivity, and proposed that African countries pool computing resources for large research tasks. He also said Nigeria’s position in the Oxford AI Readiness Index improved from 103rd to 73rd over two years. The remarks set out policy priorities and proposals; the report did not announce a funded data-park project or a regional compute facility already in operation.
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Nigeria’s Minister of Communications, Innovation and Digital Economy, Bosun Tijani, called for shared computing infrastructure and stronger regional cooperation to accelerate artificial-intelligence development.
Speaking through the National Centre for Artificial Intelligence and Robotics director at the BUILD 2026 conference in Abuja on 21 October, he said electricity, broadband, cloud capacity and access to advanced chips were major constraints.
Researchers from Ottawa, Columbia and Hanyang reviewed post-CMOS materials and device approaches for artificial neurons; the work is a literature review, not a new chip prototype.
A preprint describes an analog memory architecture that stores multiple high-dimensional vectors in superposed form, aiming to reduce the number of memory cells needed for neural-network weights and embeddings. The authors say they demonstrate vector storage and retrieval.
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