Machinepower Index / Dossiers / Nigeria
Rank 44–49 across the scoring rules tested. How this is tested
Nigeria
Diffusion Advantage — For nations that cannot build the frontier but can use it faster than anyone: import the models and win on deployment, across the state and the economy, with public consent.
How the Score, Potential and bottleneck cost are worked out: methodology.
The three layers
Energy, grids, data centres and silicon — the physical capacity to run models at scale.
Epoch AI records no large AI clusters or data centres here yet.
Models, alignment capability, standards influence and the talent that produces them.
The capacity to wire AI into the state and the economy while holding public consent.
Where the score comes from
| Layer | Sub-dimension | Basis | Score |
|---|---|---|---|
| Watts | Planning & Permitting | Assessed | 3.0 |
| Firm Power Capacity | Dataset | 3.4 | |
| Sovereign Compute | Assessed | 2.0 | |
| Hardware & Silicon Access | Dataset | 2.0 | |
| Weights | Frontier Model Output | Dataset | 3.0 |
| Alignment Capability | Assessed | 3.4 | |
| Regulatory Standing | Dataset | 7.8 | |
| Talent Density | Assessed | 4.4 | |
| Will | Policy Capacity | Dataset | 8.1 |
| Public Sector Adoption | Dataset | 4.7 | |
| Workforce Transition | Dataset | 3.8 | |
| Public Trust | Assessed | 9.2 |
Assessed marks a measurement we judged rather than took from a dataset: 5 of the 12 here.
Sources
Supply is so unreliable that data centres run their own generation.
View source →Signed Bletchley and Seoul and has a national AI strategy; no safety body.
View source →No system on the June 2026 TOP500 and no compute controlled by the state, universities or firms in Epoch AI; set at the floor.
View source →Stanford does not count Nigeria. Estimated from 1.8 million GitHub developers and Oxford Insights' diffusion pillar, calibrated on the nations Stanford counts.
View source →Feeds governance and public-sector adoption; policy capacity is listed separately.
View source →Ipsos does not cover it. Mapped to the Ipsos scale through the 28 countries both surveys cover.
View source →Feeds workforce, rescaled across 173 economies.
View source →Fossil, nuclear, hydro and other dispatchable plant; averaged with generation for firm power.
View source →Half of public trust: support for building more data centres, scored as 1 + share ÷ 10 and averaged with trust in AI.
View source →Bottlenecks
These hold Nigeria below its Potential. Its two lowest scores cost it the most: sovereign compute at 2.0 (part of Watts) and hardware & silicon access at 2.0 (part of Watts).
Assessment
Nigeria has one of Africa's largest developer bases and a fintech industry that already runs on software, but a grid so unreliable that data centres plan on their own generation.
The art of the possible
The gain is in use: finance, health and government can adopt imported models with little compute at home. N-ATLaS, an open model tuned for Nigerian languages, is a start.
Strategic play — Diffusion Advantage
Deploy imported models in the languages and services Nigerians use, and build power for compute later.
Leverage — the nearest position. No chokepoint. Its position is size: Africa's most populous country, which any model serving Africa must handle well.
Open Nigeria in the live Index → — every source behind every number, and the comparison tool.