Two hundred million people speak Swahili. It is the most widely spoken language in sub-Saharan Africa. It is an official language of the African Union.

The AI systems most people use were predominantly trained on English text. The result is predictable: Swahili queries get lower-quality responses, higher error rates, and less contextually appropriate answers.

This is not a mystery. It is a data gap.

What the data gap looks like in practice

The Common Crawl — the web scrape that underlies most large language model pretraining — is estimated to be roughly 45-50% English by volume. Swahili represents a fraction of a percent.

This means a Swahili-speaking community health worker asking an AI system for guidance on pediatric malnutrition gets a response built on a foundation of English medical literature, translated into Swahili. The clinical accuracy may be adequate.…