Disclosure: I work at GiveDirectly. This is a linkpost summarizing findings from a pilot we ran in Rwanda. I used AI to assist in writing this post, and it’s likely that >30% is AI-generated text. 

View our blog and watch a video of recipients using AI here:

https://www.givedirectly.org/the-robots-work-at-night 

Last year, GiveDirectly tested whether unrestricted access to an AI chatbot could complement cash transfers for recipients living in extreme poverty. Alongside our usual ~$1,000 one-time transfers in rural Rwanda, we offered 832 recipients access to a ChatGPT-powered chatbot via WhatsApp - a platform most already used - with no restrictions on what they could ask.

What we expected

We anticipated questions about the GiveDirectly program, help planning how to spend transfers, and basic business advice. People did use it for all of those things.

What actually happened

The more revealing pattern was how quickly recipients moved beyond program-specific questions. Across 21,000 inbound messages between November 2025 and April 2026, people used the chatbot the way people use AI everywhere: for family conflicts, sick children, market prices, and questions they couldn't easily take to anyone else. A few examples, translated verbatim from Kinyarwanda:

• "I have conflicts with the person I married."
• "Why does my child cough at night?"
• "What is the number of neighbors I should be cautious about?"
• "Who are you that you answer me?"

This isn't surprising in isolation - it mirrors how AI is used globally. But in rural Rwanda, where a community health worker, business coach, or legal aid office may be hours away or nonexistent, the stakes of that access feel different.

The timing finding

Usage increased late at night - after farm work, after children were asleep, in the quiet hours when formal services are long closed. One recipient captured it simply in a focus group: "The robots work at night." This matters because most traditional support programs - training sessions, coaching, extension services - are delivered during the day, in groups, on fixed schedules. The chatbot met people where they actually were.

Where it fell short

This is where we think the EA community's scrutiny is most valuable. Three gaps stood out:

• Language. Kinyarwanda support across major LLMs is inconsistent and often poor. Language is the first barrier to meaningful access, and it remains largely unsolved for most African languages.
• Voice. For lower-literacy users, voice notes are the natural interface. But voice functionality was slow, unreliable, and poorly adapted to this context.
• Local knowledge. Models know Kigali far better than the villages where our recipients live. The more rural the setting, the less useful the AI's answers about local markets, services, and conditions.

Open questions

We're continuing to test - a similar pilot is now underway in Malawi - but we're genuinely uncertain about several things and would value the community's thinking:

• How should we evaluate LLM utility in low-resource settings when standard benchmarks don't capture what matters - local language quality, contextual relevance, reliability on local queries?
• What's the right bar for "good enough" language support before deploying these tools at scale?

We don't think the answers will come from one organization or one pilot. If you're building, funding, or researching AI in low-resource settings, we'd welcome the conversation.