HomeGrown

Context & Motivation

AI now advises people on health, money and work, mostly through general models. Access to generic AI models alone is not enough. In a field experiment with 640 Kenyan entrepreneurs using an AI mentor, those who made changes tailored to their own business gained, while those who more often adopted generic advice did worse (Ref A). Te Hiku Media, a charitable organization belonging to Māori iwi in Aotearoa New Zealand, built speech recognition for te reo Māori and licenses its data only for uses that benefit Māori, ruling out surveillance and discrimination (Ref B). Open models can now be adapted easily, with outsized benefits for the communities it serves.

Target Audience

Communities with diverse language, culture or profession that want AI to work for them, to be paid for helping build it, and to ensure AI benefits their own people. Public services, banks and schools needing evidence their AI works for everyone they serve. AI labs wanting to reach people their models serve poorly and to become the platform community models run on. Libraries, cultural centres and associations that can host community groups.

Customer Problems

1. Fit: general models can be less reliable in a community's own language, dialect and context.
2. Fair pay: people who improve AI for their community are rarely paid, while the value goes to the model's owner.
3. Ownership: a community's language and knowledge can be used without its consent, control or a share of the benefit.
4. Getting started: most communities lack the skills, structure and funding to begin.
5. Legitimacy: anyone can claim to speak for a community, and dissenting voices within it can be shut out.
6. Concentration: when a few general models answer everyone, their blind spots reach everyone and local perspectives fade.

Solutions

1. Community groups: libraries, cultural centres and associations host groups with a standard kit: a meeting format, roles, and training to use, test and improve AI. Members are paid and gain skills and credentials.
2. Community charter: a co-op or trust owns the community's data, tests and model. It publishes its funding and authors.
3. Build on an open model: members adapt it with their own data, tests and examples of good answers, by voice, in their own language and dialect.
4. Paid from day one: members are paid first from a community fund from Frontier labs that will host the model, drawing on the share of computing that each data centre gives to work that does good in DaisyGrove; later from the model's own income, through the circular economy proposed in Everlasting.
5. Test before trust: community and general models are tested side by side. Findings that touch dangerous capabilities, such as weapons, go privately to the provider and an independent safety group, never to a public list.
6. Shared floor: every group follows common rules anchored in human rights: no tests built to target another group, care for knowledge a community holds sacred, and appeals to an independent body.
7. Licence and earn: the community decides who may use its model and for what, as Te Hiku Media does with its data. Services subscribe, labs pay to host the model or route questions to it, and the community is paid each time its model contributes to an answer (similar to the pay-per-stream model that exists for songs).
8. Fork: any group can adapt a model for its own dialect or diaspora, or start its own version if it feels unrepresented, crediting and sharing income with the original.
9. Many voices, one answer: in future, one question can be routed to several community models at once, combining their context into one richer answer, or even a shared work of art, with each community paid for its part (Ref C). This extends the model pluralism proposed in Two Seeds.

Outcome

1. Communities get AI they can actually use: it works in their own language, dialect and context, and they own and govern it.
2. A circular economy that stays in the community: income from licences, subscriptions and each contribution to an answer pays the people who build and test the model, and funds its next improvements.
3. Members gain skills and credentials while doing good for their own community.
4. AI labs reach people their models serve poorly, and becoming the trusted platform community models run on.
5. More diverse answers provided by general models because answers draw on many communities and no single model shapes every answer.

Appendix

1. Otis, N. G., Clarke, R., Delecourt, S., Holtz, D. & Koning, R. The Uneven Impact of Generative AI on Entrepreneurial Performance. Harvard Business School Working Paper 24-042.
2. ITU (2022). Te reo Māori speech technology and community data sovereignty. https://www.itu.int/hub/2022/08/ai-indigenous-languages-maori-te-reo/
3. Du, Y., Li, S., Torralba, A., Tenenbaum, J. B. & Mordatch, I. (2024). Improving Factuality and Reasoning in Language Models through Multiagent Debate. ICML. The study had several copies of one model debate their answers; combining different community models is a proposed extension.