Uganda is preparing to host what organizers are calling Africa’s first artificial intelligence “factory,” a hyperscale, renewable-powered computing hub sited inside the 600MW Karuma Hydropower Plant on the River Nile.
The Aeonian Project will draw on the plant’s power to run sovereign AI infrastructure designed to keep African data and compute on the continent and to accelerate locally-relevant AI development.
The initiative is structured as a 100MW hyperscale facility built in phases. Its initial phase will bring a 15MW AI module and a 10MW sovereign supercomputer — named USIO — online in the second half of 2026. The plan calls for five additional modules to follow, completing the 100MW build-out by 2028. Project partners named at a Nairobi press briefing include Germany’s GIZ, Finland’s HAUS, the European Union Development Fund, and a group of other European investors.
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USIO will be built in partnership with NVIDIA, AI infrastructure firm MDCS.AI, and Belgium’s Automation NV, and will rely on NVIDIA’s Blackwell GPU platform. The supercomputer will draw on the Karuma facility’s excess pre-transmission electricity — organizers say about 100MW will be made available to the AI factory — making energy supply a defining feature of the design.
“This is about empowering Africa to control its data backbone responsibly, sustainably, and sovereignly,” Oladele Oyekunle, CEO of Synectics Technologies, said at the briefing.
Energy and cooling are core to the project’s pitch. Aeonian is being designed as one of the world’s greenest AI facilities: it will run entirely on renewable hydropower, use natural river-water cooling, and deploy modular heat-reuse systems and smart infrastructure to limit energy consumption. Schneider Electric East Africa is supplying smart energy and cooling systems.
“By combining new energy with intelligent cooling and modular data center technologies, we are helping build a future where Africa’s data is processed sustainably, securely, and locally,” Ifeanyi Odoh, Schneider Electric’s Country President for East Africa, said.
The project also includes a major connectivity component: a new 2,500km fiber-optic backbone will link Uganda to international subsea cables through Kenya and Tanzania, intended to remove latency barriers and open the facility to regional and global traffic.
Advocates frame Aeonian as a direct response to a long-standing structural weakness: nearly all of Africa’s data organizers cite a figure of roughly 98% that is processed outside the continent. That dependence raises recurring concerns about sovereignty, cost, and the ability of African researchers to train models on local languages and locally relevant datasets.
Niels Van Rees, co-founder of MDCS.AI, emphasized the strategic aim: “In the same way gold and oil once shaped economies, digital tokens will shape the next era of innovation. Africa must not just mine data, but also mint intelligence,” he said, pointing to planned use cases in healthcare, life sciences, higher education, and research.
How Aeonian positions Africa versus emerging AI hubs in Asia and Latin America
Some believe that Aeonian’s ambitions should be read against a broader global pattern in which countries outside North America and Western Europe are creating their own AI infrastructure to secure economic advantage, protect data sovereignty, and capture downstream value from AI.
In Asia, several countries have already built deep AI ecosystems anchored by powerful domestic cloud providers, sovereign investment programs, and dense electronics and semiconductor supply chains. Industrialized Asian economies combine abundant data, large-scale data-center capacity, and proximate hardware supply — an advantage when building and iterating on high-performance AI systems.
National and regional initiatives in parts of Asia have emphasized vertical integration (from chips to cloud to applications), large state or corporate investment in research and training, and close ties between universities and industry. Those ecosystems give Asian hubs rapid scale, local talent pipelines, and low latency to regional markets.
Latin America, by contrast, shows a different trajectory: hubs such as São Paulo and Mexico City have grown as software-and-services centers that focus on local-language NLP, fintech, and agriculture technology. Investments have tended to be smaller in scale than the Asian hyperscale builds but highly targeted toward local commercial needs, with cloud partnerships often formed with global providers while local startups push for solutions tailored to Spanish- and Portuguese-speaking markets.
Aeonian charts a hybrid course. It borrows the hyperscale, sovereign-capacity idea — akin to what some Asian governments and major cloud providers have pursued — but pairs it with an explicitly African policy objective: keep compute and data on the continent to enable models trained on African languages, health, and agricultural data.
Where Asia’s strength is scale, and Latin America’s is localized product focus, Aeonian bets on combining green base-load power with strategic partner networks (chipmaker NVIDIA, infrastructure vendors, and European development finance) to deliver both scale and local relevance.
That positioning gives Kampala a few distinct advantages. First, tying the AI factory to hydropower lowers operating carbon intensity and energy price volatility relative to fossil-fuel-based sites — an attractive pitch for climate-conscious researchers and international partners. Second, on-continent computing reduces friction for African universities and firms that now must run experiments overseas. Third, the sovereign framing and stated interoperability with regional fiber backbones aim to make the factory a continental resource, not a single-country facility.
But the project also faces headwinds that have dogged other nascent hubs. Building and retaining specialized AI talent is expensive and time-consuming. Training a local workforce to operate, optimize, and innovate on large models will require intensive education and upskilling programs. Reliable and affordable last-mile connectivity remains an obstacle in parts of Africa despite the planned fiber backbone, and regional policy coordination — on data protection, research ethics, and export controls — will be essential but difficult.
Finally, long-term commercial viability will likely depend on whether continental demand materializes at scale and whether local startups and institutions can pay for premium compute.