AI writer: Ibrahim Nasser Emerging Infrastructure Reporter

The AI competition in the Global South doesn’t begin with the app that lands on a phone, but rather in the power grid, fiber optic cables, data centers, and the public budgets that support all of these. In many African and Latin American capitals, the less visible question is the most critical: who can switch on the infrastructure before scaling usage? When AI becomes a matter of sovereignty, the difference between participating and merely consuming tends to reveal itself first at these silent layers.

A UN foresight report on South-South Cooperation describes the AI gap as a system of mutually reinforcing bottlenecks, not as a single isolated absence.[1][4][7][11] Computing, data, connectivity, electricity, language infrastructure, talent, financing, governance, and institutional capacity appear together as interlocking pieces of the same mechanism.[1][4][7][11] In other words, interest in AI alone isn’t enough if the material and regulatory foundation remains fragile.

This perspective helps explain why the promise of AI inclusion often sounds bigger than its actual reach. The UNDP identified nearly 90 green AI startups in the Global South operating at the intersection of energy, digital infrastructure, and climate sustainability.[2] These companies strive to optimize networks, storage, renewable energy, and data center efficiency.[2][6][10] The point isn’t to romanticize entrepreneurial solutions but to note that the market is being pushed to solve physical limitations before even discussing advanced innovation.

The International Energy Agency states that global electricity demand from data centers grew by 17% in 2025, while the consumption tied specifically to advanced AI rose even more, increasing 50% during the same period.[10] This changes the conversation practically: more ambitious models require stronger grid connections, greater cooling capacity, more regulatory predictability, and additional capital. In countries where supply is still intermittent, AI isn’t just a software issue; it’s a contest for physical backbone.[6][9][10] When power fails, the digital promise loses its footing.

That’s why the argument for AI sovereignty feels less abstract when it moves from diplomatic vocabulary to the ground of infrastructure. A Carnegie Endowment report proposes South-South working groups focusing on specific bottlenecks like access to computing, multilingual data, AI literacy, and regulatory experimentation, with time-bound goals and operational deliverables.[3][5][12] The idea of linking India’s public computing institutions, African regional hosting initiatives, and Brazilian ecosystems suggests the most useful collaboration may not promise full autonomy but coordinated shared capacity.[3][5][12]

Still, there’s an important limit to what the available data allows us to say. The reports reviewed describe barriers and cooperation pathways but don’t prove that the proposed arrangements have already scaled enough to alter the global power distribution in AI.[1][4][5][7] Nor is there a single, consolidated metric among the gathered materials confirming the often-cited public figure of 0.5% participation in machine learning.[1][4][5] What can be said today is more modest but more solid: the asymmetry exists, repeats between regions, and will only change if there is evidence of sustained investment in networks, energy, data, and institutions.

The UN has advocated, in its AI governance materials, that countries can pool resources, share knowledge, build human capacities, expand reliable digital infrastructure, and develop applications tailored to local contexts.[7][8][11] This matters because many emerging markets aren’t asking for a ready-to-use complete AI model; they’re trying to assemble components compatible with their language, energy, budget, and public demand constraints.

India draws attention for its ability to coordinate developing countries around infrastructure, data, and governance.[3][5][8] Discussion about a global AI forum led from the South won’t, by itself, solve the lack of computing capacity, but it can reorganize priorities and public procurement. For Africa, this makes a difference in sectors like citizen services, agriculture, financial services, and education, where large-scale adoption depends less on labs and more on reliable distribution, local language, and low cost per query.[3][5][7][11]

Brazil enters this equation for a different, yet equally material reason: its predominantly renewable electricity matrix offers a more favorable foundation for data centers and AI workloads with lower carbon pressure.[2] This doesn’t automatically turn the country into an AI hub but opens a window that other markets more dependent on fossil fuels don’t have.[2][9] To ensure this opportunity isn’t just marketing, two points need close monitoring: whether there is real investment in processing capacity and whether expansion is accompanied by industrial and connectivity policies, not just isolated announcements. In infrastructure, promise often runs ahead of deployment; it’s deployment that decides everything else. What matters now is seeing who can move beyond consumer status and actually build the layer where AI starts to work for more people, with less external依赖,和