What 21,000 WhatsApp messages reveal about AI utility in extreme poverty contexts

GiveDirectly piloted an AI chatbot via WhatsApp for 832 cash transfer recipients in rural Rwanda, analyzing 21,000 messages from November 2025 to April 2026. Recipients used the chatbot for personal and practical issues beyond program questions, with usage peaking at night when formal services were unavailable. The pilot revealed AI's potential to complement cash transfers in extreme poverty contexts, though gaps in accuracy, privacy, and access remain.

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 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: 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: 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: 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.