AI infrastructure built in Lagos
Frontier model safety refusal collapses 55 points from English to Igala on matched harmful prompts. The corpus that closes that gap — native preference data from owned African workforce, processed on sovereign GPU — does not yet exist. Until now.
Every AI system on earth runs on human feedback. The constraint is not compute — it is genuine human intelligence at scale. Current frontier models refuse harmful prompts ~90% of the time in English. In Yoruba, Hausa, Igbo, and Igala, that refusal rate collapses to 35–55% on matched prompts.
The paper formalizes this as Refusal Centroid Drift: safety-alignment representations are anchored to English token sequences and do not transfer cleanly to tonal, low-resource West African languages. The same gap runs the other direction: multilingual preference data lifts win rates across all 23 languages in the training set, not just the targets.
If any layer is separated, someone else controls whether the teaching continues. Together, they make the generative future self-sustaining.
Nigerian annotators trained on tonal accuracy, dialect identification, and cultural reasoning. First Igbo-origin DPO expert annotators onboarding now via SkillUpImo's trained graduate base and Masakhane networks.
The first open Igbo-origin DPO dataset, sourced from 1,500+ native Igbo proverbs (ilu) as reasoning prompts. Not translated English. CC-BY-4.0 public sample, commercial tier for frontier labs.
64× A100 80GB SXM4 at Tier III Lagos datacenter. Colocation locked. Solar-powered. +35% sovereignty premium for regulated customers — banks, NDPC, government.
This is not just infrastructure. It is the economic argument for African language AI built by Africans, for Africans, on African soil.
The infrastructure is being built in Lagos. The workforce is being trained now. The data layer is being released. The window is open — and it will not stay open indefinitely.