The Great Leapfrog: Why AI Must Be Built as Public Infrastructure
The debate on AI governance is currently stuck in a binary loop: either we follow the “restrictive” models of the North, or we face the “Wild West” of unchecked deployment. But for the Global South, there is a third way. It is a path that bypasses the limitations of the past and builds a future defined by Digital Public Infrastructure (DPI).
In 2026, the real challenge for Global South leaders is not just regulating AI; it is operationalizing it. We must treat AI not merely as a commercial product, but as a public utility—a foundation upon which we can build education, healthcare, and economic justice.
The DPI×AI Convergence: A New Development Paradigm
Digital Public Infrastructure (DPI)—the interoperable, open-source systems that facilitate identity, payments, and data exchange—has been the quiet success story of the Global South. Now, as we integrate AI into these “rails,” we see a historic opportunity to leapfrog legacy systems entirely.
Why does this matter for governance? Because governance is embedded in infrastructure.
If we build our AI systems on top of DPI, we ensure that:
- Consent is Baked-In: Citizen-centric AI requires identity verification that preserves privacy by design.
- Interoperability is Mandatory: We prevent “vendor lock-in” by global tech giants, ensuring our systems remain flexible and locally managed.
- Governance is Verifiable: We can audit how AI agents interact with public records, ensuring fairness and accountability are not afterthoughts but system requirements.
From Data Extraction to Data Commons
A common critique of Global South data practices is that they are “extractive”—our data fuels Northern models, yet we receive none of the economic or developmental dividends.
To govern this, we must shift from Data Harvesting to Data Commons.
| Traditional Model (Extractive) | The DPI×AI Model (Sovereign) |
| Data is scraped and privatized. | Data is treated as a collective asset. |
| Models are optimized for Western values. | Models are trained on regional linguistic/cultural data. |
| Access is gated by high subscription costs. | Access is democratized through public API gateways. |
| Governance is opaque and distant. | Governance is embedded in open-source audit logs. |
When we establish regional data exchanges that are governed by clear, locally-authored standards, we turn our “data vulnerability” into a “sovereign competitive advantage.” We stop being the training ground for other people’s AI and start becoming the architects of our own.
Solving for the “Context Gap”
The most significant failure of imported AI models in the Global South is the Context Gap. An AI trained on Silicon Valley healthcare data will struggle to diagnose illnesses in a rural clinic in Sub-Saharan Africa or Southeast Asia, where symptoms, diagnostics, and patient history exist in vastly different contexts.
Governance, therefore, must mandate Contextual Validation.
- Algorithmic Auditing: Any AI deployed in public service—be it for credit scoring, disease prediction, or agricultural advice—must undergo impact assessments that prove efficacy within our specific environmental and social realities.
- Linguistic Equity: It is no longer acceptable to deploy models that only “think” in English. Our frameworks must demand—and incentivize—the inclusion of local languages and vernaculars as a prerequisite for market entry.
A 3-Step Strategy for Policymakers
If you are a policymaker or a tech leader looking to leave a mark, move your focus toward these three actionable priorities:
1. Build “Compute Sovereignty”
Governance is hollow without infrastructure. We need to pool regional resources to build localized GPU clusters and data centers. If we cannot host our own models, we cannot truly govern them.
2. Standardize Interoperability
Don’t write laws that demand the impossible. Write regulations that mandate interoperability. Require that any AI system operating in your jurisdiction must be able to “talk” to local DPI systems via open, secure APIs. This forces competition and prevents any single company from monopolizing your national data.
3. Incentivize Local “Model-Building”
Stop just importing LLMs. Provide tax credits, grants, and public-sector procurement contracts to startups that are training models on local datasets. Make the “Buy Local” movement a central pillar of your AI strategy.
The Bottom Line: Leadership is Choice
The 2026 UN Global Dialogue on AI Governance is signaling a shift: the world is waking up to the fact that the Global South will host the majority of the world’s population and workforce by the end of the decade. We are not just the “future market”; we are the “future context.”
We are no longer “rule-takers” waiting for the North to tell us what is safe. We are the builders of the most inclusive, resilient, and human-centered AI infrastructure the world has ever seen.
The call to action is simple: Do not build walls; build rails. Let us create a digital landscape that doesn’t just process information, but empowers our citizens. Let us lead not by demanding a seat at the table, but by building the table ourselves.