A $400 Million Nonprofit Wants to Build the Public Internet of AI
Current AI is handing out grants, shipping offline devices, and fighting to keep AI from becoming a private club. Here is what it is building, who is paying for it, and why a farmer in rural India is the whole point.
Key points
- Current AI, a nonprofit founded in February 2025, has secured $400 million in committed funding from governments, foundations, and companies including DeepMind and Salesforce.
- France seeded the organisation with $100 million as the first major contribution.
- Current AI allocated $3.2 million in grants last month to four organisations working in Kenya, Lebanon, and the Brazilian Amazon.
- Its Suno Sutra device runs AI across 22 Indian languages without an internet connection.
- Alpha Chat, an open-source chatbot anyone can inspect and modify, was built in seven weeks by ten organisations and launched last week in Geneva.
Picture a smallholder farmer in rural India. Her crops are failing. She photographs the sick plant, but every useful search result comes back in English, a language she does not speak.
That single image is the founding argument of Current AI, a nonprofit that wants to do for artificial intelligence what Tim Berners-Lee did for the web: make it free, open to inspection, and available to everyone. The organisation, first reported by TechCrunch, was founded in February 2025 by Martin Tisne. CEO Ayah Bdeir joined in January, having previously led Mozilla's AI strategy and founded littleBits, a STEM education company that sold to Sphero in 2019 after reaching millions of children.
The French government committed $100 million to seed the fund. Ford Foundation, MacArthur Foundation, Google DeepMind, and Salesforce followed, bringing committed funding to $400 million. "They're not investors; they're funders," Bdeir told TechCrunch. No one expects a financial return. That's a genuinely different model from everything else we've covered at this funding level: our story on General Compute's $400 million chip deal two days before this organisation went public showed just how differently private capital deploys the same dollar figure.
What is Current AI actually building?
At the India AI Summit in February, Current AI partnered with Bhashini, the Indian government's AI language division, to create Suno Sutra, Hindi for "listening chronicles." It's a pocket-sized hardware device that runs AI models across 22 Indian languages with no internet required. The device is open-sourced so developer communities can build on it.
Last month it awarded $3.2 million across four grantees. Masakhane in Kenya is building AI training datasets in more than 50 African languages for health and farming. Lebanon's Institute for Worldmaking is digitising Arab cultural history into databases controlled by communities, not tech companies. Brazil's Portal sem Porteiras is building offline AI tools with Indigenous Amazon communities, keeping all data inside those territories. Kenya's African Internet Rights Alliance is developing audit tools to hold AI systems accountable across the continent.
Earlier this month in Geneva, Current AI launched Alpha Chat, an open-source chatbot assembled in seven weeks by a coalition of ten organisations including Hugging Face, Mozilla, and MIT Media Lab.
Who actually controls the data?
Bdeir is direct: not a Silicon Valley company. Current AI stores models and data locally, brings community experts in before anything is designed, and writes consent protocols into every project so communities can stop the process at any point.
None of the four grantees has fully solved data ownership yet. Bdeir calls that the point. She notes that for Indigenous languages, missionary Bible translations often become AI training data before communities have agreed to anything.
Big Tech offers multilingual products, but Bdeir draws a clear line. "Big tech builds multilingual models to expand their market," she told TechCrunch, "regardless of consent or context."
Here's the real test to watch: whether public funding actually keeps these models free to use long-term, or whether infrastructure costs eventually force the same trade-offs private labs already make. The farmer in rural India is the argument. The hard part is making the economics work well enough that she's still the point five years from now.
