Narratives for the Future: What the Global Majority Is Really Saying About AI Autonomy, Regionalization, and Ethics

The current conversation around artificial intelligence in the Global Majority has, for too long, been framed as a race; a race centred on the rapidly evolving nature of AI and emerging technologies; a race premised on AI’s inevitability and the urgent need for the Global Majority to catch up. This urgency is largely skewed toward regulation and adoption.

On 25 May 2026, the MERL Tech Initiative’s Community of Practice hosted a conversation on AI autonomy, regionalization, and ethics, led by Chenai Chair, Director at Masakhane African Languages Hub, Wayan Vota, ICT4D strategist and founder of ICTworks, William Tjhi, Deputy Director, AI Products at AI Singapore and Maria Luciano, Tech Policy Analyst. The discussants unpacked what “AI sovereignty”  means for them and who it truly serves. They debated whether it can center people’s rights and well-being, whether it fuels a harmful global AI race, and whether it genuinely challenges Big Tech’s dominance or quietly reinforces it. They also questioned whether locally built AI can serve as a real alternative to giant tech systems, and what sovereignty could offer countries in the Global Majority as a path toward greater independence in knowledge and technology.

This blog captures the key threads of that dialogue and explains why they matter.

Why This Conversation Matters for Global Majority Digital Futures

A narrative circulates across development, policy, and technology spaces: AI adoption is simply the next logical step, and you either get on board or get left behind. Maria pushed back on this framing directly, noting that when people are told there is no choice to be made, the question of accountability disappears. If the AI race is inevitable and adoption is already decided, then the communities most affected never sat at the table where that decision was made.

This conversation emphasized how narratives shape technological realities. The speakers drew a sharp line among narrative-level framing, the legal and regulatory frameworks that narratives produce, and the real-world infrastructure that ultimately enforces or undermines those policies. When the dominant narrative is “AI is inevitable,” resulting policies tend to treat inclusion as a footnote, and the infrastructure built to enforce those policies carries the same blind spots. Accountability does not disappear in a single event; it erodes layer by layer, beginning with how we talk about technology in the first place. (Read more from Maria on this.)

Communities across the Global Majority already experience the downstream effects of AI systems built without their input, trained on data extracted without their consent, and deployed in languages never designed to carry the weight of their realities. As Chenai noted, any technology that affects how people access information or exercise their social and cultural rights must also be able to connect with them on their own terms. The choice of language, the choice of modality, the choice to engage or not, these are not technical preferences but foundational design choices. The central question, therefore, is not whether AI is inevitable, but who determines its terms. That question must be present throughout the Global Majority AI conversation: not only at the implementation stage, but at the levels of narrative and design. 

The Sovereignty Narrative: Local Models Versus Big Tech

The framing of sovereignty must be honest about what is actually achievable and where real leverage lies. The speakers moved the conversation on AI sovereignty critically beyond its most marketable framing: competing with hyperscalers on foundation model development is not realistic. However, governments and communities can build with genuine purpose and genuine control in targeted areas.

Wayan put the numbers plainly. Hyperscalers in the United States are projected to spend approximately $700 billion in capital expenditure in 2026 alone, with projections exceeding $1 trillion over the next three years. By comparison, the European Union has announced a $2 billion AI investment, Canada has committed $2 billion to a sovereign AI strategy, and India has allocated $1.5 billion to its AI mission. Even the most well-resourced governments in the world cannot compete with these hyperscalers at their own game. For governments in the Global Majority, the gap represents an entirely different category of problem.

What made this discussion particularly sharp was the observation that the loudest voices promoting sovereign AI are often those with the most to gain from governments attempting to build it. When Nvidia, the world’s largest chip manufacturer, tours the world’s capitals telling each government they need their own sovereign AI, and that same manufacturer supplies the chips required to build it, the alignment of interests deserves to be interrogated. 

An alternative exists in small language models. Small language models optimized for specific community needs, edge computing solutions that operate on devices without expensive cloud infrastructure, and domain-specific models in sectors such as health, agriculture, or legal aid represent areas where hyperscalers hold no particular advantage and, frankly, limited incentive to invest. These may be precisely where autonomous and sovereign AI can take root.

Realistic sovereignty, then, looks like this: build what you can control, contribute where you can influence, and be honest about where you cannot compete. Governments should direct their energy toward what is within reach, thoughtful procurement policy, domestic data governance, investment in local infrastructure, and support for community-led language initiatives, rather than expensive sovereign compute investments that primarily benefit hardware vendors. The conversation also surfaced what sovereignty discussions too often omit entirely: people. Whether the lens is state security, economic competition, or cultural and linguistic preservation, communities—particularly marginalized ones—are rarely the subjects of these conversations.

Autonomy Is Not Sovereignty: The Practical Distinction

One of the most clarifying moments in the discussion was the framing of autonomy as distinct from sovereignty in application. The distinction is not limited to definition; it lies in what you build, how you resource it, and who you design it for. Sovereignty, as the speakers explored, anchors its ambitions at the level of the state: national compute, national models, national data governance. Autonomy addresses a more immediate and survivable question: if access to an external system disappears tomorrow, can you still function?

William offered a practical framework for thinking about whether governments and institutions should build or buy AI capability. The buy-first-then-build logic was endorsed as a reasonable general approach, map your people’s needs through what already exists, then develop targeted domestic capability for what is not being served. The timing of the shift from buying to building matters enormously and varies by context; for some countries and communities, waiting too long means losing the generation of talent and institutional knowledge needed to build at all. (See the full paper here.)

The goal of building domestic AI capability is not purity of origin but negotiating power. Chenai described the reality of depending on compute infrastructure provided by large technology companies under time-limited agreements. When that agreement ends, the question becomes: what do you have left? If the answer is nothing, then every product built, every community served, and every promise made was contingent on a boardroom decision made without your presence. Autonomy, in this framework, means having enough capability to survive an interruption, even if you choose not to exercise that capability every day. This has direct implications for how organizations think about community guardrails and AI safety in practice. Well-intentioned guardrails tend to protect those closest to the infrastructure. If AI autonomy is to offer meaningful protection for the most marginalized communities, the design of those guardrails must explicitly account for who sits furthest from the system and what reaching them would actually require, in terms of connectivity, cost, language, and trust.

A hybrid approach emerged as the clearest practical recommendation: work with multiple AI providers, maintain core capability in areas of genuine community need, and build collaborative infrastructure with others who share your goals. No single country or community organization can do this alone. The future of autonomous, community-serving AI in the Global Majority will be built through deliberate cooperation, across institutions, across the Global South, and with aligned global partners.

The Outlook: Dreaming Carefully, Building Durably

Several threads converged as the conversation turned to future outlooks.

First, there was a consistent return to the value of focusing on what is within reach. A well-built, community-governed model in a specific language and domain can serve its people with a depth and responsiveness that no general-purpose model will ever prioritize. Hyperscalers will not build a model that captures the slang, dialect, or specificity of a language spoken by smaller populations, but the people who speak that language can.

Second, the call for locally defined, alternative evaluation metrics was emphasized. If Global Majority communities develop assessment techniques for their own AI systems that are systematic, not prohibitively expensive, and rooted in what actually matters locally, they gain a form of influence that does not require competing at the infrastructure level. Communities that can assess what works and what does not, by their own standards, change the terms of the conversation.

Third, the technical complexity of AI is not neutral. Keeping the language of AI governance inaccessible to non-specialists is itself a political choice, a way of ensuring that those most affected by these systems remain outside the room where decisions are made. Education and literacy, not just digital literacy but narrative literacy, the capacity to recognize and contest the stories being told about technology, are as important as compute or data in determining who benefits from AI’s development.

Finally, environmental sustainability cannot keep being deferred. The Global Majority’s natural resources and human labor are fueling the AI boom, often at the risk of further marginalization and for the benefit of Big Tech. The infrastructure required to build and maintain large AI systems demands enormous resources, frequently extracted in ways that replicate the very patterns of exploitation that communities in the Global Majority are working to escape. A vision of AI that is sovereign and community-serving but environmentally catastrophic is not one worth building toward.

Closing Reflection

Collaboration among institutions in the Global Majority, as we continue to interrogate autonomy and sovereignty, is a necessary first step: collectively naming what is at stake and pushing back on narratives that would reduce communities to perpetual adopters of Big Tech’s outputs. Strategizing collectively and charting alternative paths to reclaiming agency in AI is a practical starting point. It may one day produce shared language, shared values, and shared pushback strategies powerful enough to have communities shape how technology is built, countering the mainstream narrative that digital futures depend on Big Tech.

AI Use Disclosure: The blog utilized Claude Sonnet 4.6 for copyediting, grammar and syntax. The copyedited content was reviewed thoroughly and further edited by the author. The content remains the author’s original ideas and reflects the author’s thoughts and style of writing.