94 Million Hausa Speakers, and AI Still Barely Understands Them. What Three Years of Grassroots Work Taught Me.

Hausa is spoken by close to 94 million people in Nigeria alone, with tens of millions more across Niger and the wider Sahel. Nature has reported that ChatGPT and LLMs correctly understands only 10 to 20 percent of sentences written in Hausa.

I've spent the last three years trying to close that gap. First alone, then with a community I founded in Bauchi State that's since grown past 500 people. This isn't really a project retrospective. It's an honest account of what it actually takes to build language technology for a language the industry has quietly decided isn't worth the training data, and what leading the people doing that work with me has taught me. Why the gap exists, and why scraping the web won't fix it

The easy explanation for Hausa's AI gap is "not enough data." That's true, but it undersells the problem. Hausa has technical properties that make naive data collection actively misleading, not just insufficient.

Start with orthography. Standard written Hausa uses three hooked consonants, ɓ, ɗ, and ƙ, that don't exist on a standard keyboard. In formal, edited text they're there. In the informal Hausa that actually dominates the web (social media, SMS, WhatsApp, the actual keyboards people type on) they're routinely flattened to plain b, d, and k. Scrape the open web for Hausa and you get a corpus quietly missing phonemic information in a large share of its examples, and that gap propagates into anything trained on it, from tokenizers to text to speech models.

Then there's script. Hausa has been written in two systems for over a century: Boko, the Latin based script that's now standard, and Ajami, an Arabic based script still used in religious and literary contexts. A dataset built only from Boko sources is already excluding a real slice of the written language.

Then code-switching. Everyday Hausa, especially online, mixes in English freely and carries a long history of Arabic loanwords from Islamic scholarly tradition. A model that treats Hausa as a clean, isolated language misses how the language is actually used in practice.

Put those together and "low-resource language" starts to look less like "not enough Hausa exists" and more like "the Hausa that's easy to scrape is a degraded sample of the Hausa people actually speak." Standard multilingual tokenizers, trained on that same degraded sample, fragment Hausa words into far more subword pieces than an equivalent English sentence needs. That makes inference more expensive and generation noticeably worse, since the model has fewer clean examples to learn from per token.

That's the actual problem. Not "AI doesn't know Hausa." AI has been fed a thin, flattened version of Hausa, and it shows. Where I actually started, and why not with an LLM

I founded AI Bauchi in October 2022 out of Abubakar Tafawa Balewa University, with a mission that hasn't changed since day one: make AI indigenous and accessible in Bauchi State, not just consumed from outside it. The language work grew out of th…