THE FUTURE IS FOR EVERYONE. IS IT REALLY?
Vania La Rocca contrasts Mark Zuckerberg’s manifesto with what is already happening far from Silicon Valley. In Uganda, a locally built AI tool based on an open Chinese model is serving farmers in their own languages more effectively and affordably than leading American systems.
by Vania La Rocca
Last year, in Kampala, Ernest Mwebaze went looking for an artificial intelligence that could speak Uganda's languages. A former research scientist at Google, he tested American models against Chinese ones to see which could handle the country's dozens of tongues.
A Chinese model won.
It handled Uganda's dozens of languages better than anything from Meta or Google; it cost far less, and he could retrain it on his own data. He built a tool called Sunflower on top of it. Today, a coffee farmer named Muganzi asks Sunflower about his crops in his own dialect and receives an answer. The model underneath, the New York Times reported this month, came from Alibaba. On August 10, Mark Zuckerberg published over 6,000 words describing a future in which every child on Earth has a personal tutor with a PhD in every subject. In Uganda, something like that tutor arrived. It was not his. It was not American, and it did not come from Silicon Valley.
The promised future
Zuckerberg's essay, "The Future is for Everyone", does not make its central promise about efficiency. It is invention. "Invention, not automation," he writes, will be the greatest contribution of the technology Meta is spending tens of billions to build. (Is it, really?) Meta's founder describes AI that helps scientists cure disease this century, that discovers new drugs, that teaches every child. He argues that this future should be open, that spreading the technology widely is safer than concentrating it, and Meta released a new open-weight model the same day to make the point. Take the argument at face value, because much of it is right. The purpose of a general-purpose intelligence should be to invent what we cannot yet invent. To buy us capability we do not have. To give us, in the most literal sense, more. And if it is going to be for everyone, everyone has to be able to get their hands on it. Now look at what has actually been shipped.
What the machine does
At scale, what has shipped is mostly automation, not invention. In 2025, the MIT initiative NANDA studied enterprise AI and found that 95 per cent of company deployments had no measurable effect on the bottom line, despite spending 30 to 40 billion dollars. That is a verdict on enterprise return, not on the frontier of science, but it shows where the money went: into making existing office work marginally cheaper.
