Rethinking ‘AI-Native’: Why Local Context Isn’t Optional

In today’s tech world, every innovation claims to be “AI-native.” But too often, it means native to Silicon Valley—not to the context of Nairobi, Jakarta, or Bogotá. Across Africa, Southeast Asia, and Latin America, the dominant AI narrative still revolves around retrofitting tools and models built in and for fundamentally different contexts. 

According to the 2022 Oxford Insights Government AI Readiness Index, only 21 out of 32 surveyed African countries considered AI a national development priority, and Sub-Saharan Africa remains the lowest-ranked region globally on AI readiness across governance, infrastructure, and data capacity. In Southeast Asia, only Singapore, Malaysia, and Thailand surpass the Asia-Pacific average on AI preparedness, while the rest lag in digital infrastructure and skills. But as the Global South faces rising climate volatility, rapid urbanization, and shifting demographic patterns, a new paradigm is taking shape, one where AI is not merely adopted, but rooted.

The Mirage of Infrastructure Assumptions

Most AI solutions are designed with invisible prerequisites: reliable connectivity, clean structured data, and scalable compute on demand. For much of the world, these conditions don’t exist. In fact, the absence of such infrastructure is often misinterpreted as a deficit, when in reality, it's a design brief. 

In 2022, only 45% of Sub-Saharan Africans owned a mobile phone, and cloud access is also limited—South Africa is the only country in the region with commercial 5G and a top-tier cloud provider presence. In Latin America, a 1GB data plan can cost up to 8–10% of monthly income for low-income households, far above the 2% affordability threshold set by the ITU.

The next wave of AI innovation will not come from adapting to the Global South, but from emerging within it. Locally trained models, region-specific datasets, and resilient compute solutions are not just aspirational—they are necessary.

Ground-Up, Not Top-Down

To be AI-native in Nairobi or Manila means something radically different than it does in San Francisco. It requires starting from the ground up, not the cloud down. This includes:

• Digitizing raw, often analog data sources to reflect real-world conditions
  
• Deploying edge compute systems that operate in power-constrained or offline environments
  
• Embedding contextual knowledge—language, history, socio-economic patterns—into models
  
• Retaining local ownership and agency over how AI is built, used, and monetized

The Operating System, Not the App

The Global South doesn’t need more apps. It needs architecture.

That architecture may look like containerized data centers running on solar power, or APIs offering access to previously siloed environmental data. It may involve foundational models trained on drought patterns from East Africa, or public-private data cooperatives designed to prioritize resilience over scale. This is the operating system for digital sovereignty, a layered infrastructure that enables not just AI adoption, but AI ownership.

But infrastructure alone isn’t enough. The intelligence itself must be grounded in local context. That means building public-private data cooperatives that protect against extractive AI deployment and reinforce community resilience, not just commercial scale.

These are some preconditions for a new kind of intelligence—one that enables data ownership, policy agility, and economic self-determination. They form the operating system for digital sovereignty: a layered stack of tools, governance, and capacity that lets nations and communities shape AI on their own terms in the Global South.

Data is one of the most valuable natural resources a country holds today. But its true power is only unlocked when refined into intelligence, by and for the people it represents. That requires local AI infrastructure: systems capable of transforming raw, often fragmented data into insights that serve national priorities.