AI Governance Cannot Be Built Without the Global South at the Table

A Call to Action, But From Whom?

In mid-July, more than 200 economists and AI researchers - including 16 Nobel laureates - signed an open letter organized by the Stanford Digital Economy Lab. The message was urgent: artificial intelligence may trigger an economic transformation "larger than the Industrial Revolution" compressed into a fraction of the time, and humanity must act immediately to build institutions capable of steering it. The framing was powerful, the stakes enormous, and the credentials impeccable.

Then came the signatory list. Four in five names belonged to researchers and institutions based in North America and Western Europe. Asia, Africa, Latin America, and the Middle East - home to more than 80 percent of the world's population - were represented by a sliver of voices. At DailyTechWire, we've tracked the composition of AI policy forums, standards bodies, and research coalitions across the region for three years. The pattern is consistent: the people designing the guardrails for AI are overwhelmingly from the same dozen countries, while the technology's economic and social consequences will land everywhere.

This is not an academic footnote. It is a structural flaw in how the global conversation about AI governance is being conducted, and it carries real costs for markets, labor systems, and innovation trajectories from Jakarta to Nairobi.

Why Geography Matters in AI Policy

Artificial intelligence does not arrive in a vacuum. It lands in economies with different industrial bases, labor protections, data privacy regimes, and regulatory capacities. A policy framework optimized for Silicon Valley or Brussels may be unworkable - or actively harmful - in Bengaluru, São Paulo, or Lagos.

Consider the debate over compute thresholds for frontier model regulation. Proposals to restrict access to high-performance chips or mandate safety testing at specific FLOP counts assume that governments possess both the technical expertise and enforcement infrastructure to monitor compliance. Many do not. Export controls on advanced semiconductors, designed in Washington and echoed in allied capitals, have already reshaped supply chains across Southeast Asia and constrained research capacity in universities from Hanoi to Manila. Those decisions were made with minimal input from the governments and institutions directly affected.

Or take labor market impacts. The Industrial Revolution analogy invoked in the Stanford letter is instructive, but the historical parallel cuts both ways. Industrialization created vast wealth, but it also displaced millions of workers, and the institutions that eventually cushioned those shocks - unions, social safety nets, public education systems - took generations to build and were shaped by the specific political economies of Europe and North America. Emerging markets today face a compressed timeline and different starting conditions: larger informal sectors, weaker labor protections, and less fiscal headroom for retraining programs. The policy tools that work in Germany may not translate to Indonesia.

The Representation Gap in AI Research

The geographic imbalance in AI governance discussions mirrors a deeper asymmetry in research capacity. The majority of cutting-edge AI labs, the largest training clusters, and the most cited publications are concentrated in a handful of wealthy countries. That concentration is self-reinforcing: talent flows toward resources, resources flow toward incumbent hubs, and the resulting knowledge base reflects the priorities and assumptions of those hubs.

This matters for technical design, not just policy. Bias in training data is a well-documented problem, but bias in research agendas is less visible and harder to correct. Which languages receive investment in natural language processing? Which medical datasets inform diagnostic models? Which agricultural conditions shape precision farming tools? The answers correlate strongly with where the researchers sit and which funding bodies write the checks.

At the same time, pockets of world-class AI research exist across the Global South - Tsinghua and Peking University in China, the Indian Institutes of Technology, the African Institute for Mathematical Sciences, labs in São Paulo and Buenos Aires. These institutions often work under tighter budget constraints and with less access to frontier compute, but they also grapple with problems that Western labs rarely prioritize: multilingual models for low-resource languages, edge inference for unreliable power grids, privacy-preserving architectures for environments with weak data protection enforcement.

Excluding those researchers from the governance conversation means excluding the contexts in which AI will be deployed at the largest scale.

Who Sets the Standards?

Standards and norms, once established, are sticky. The institutions being built now - model evaluation frameworks, safety benchmarks, licensing regimes - will shape the AI ecosystem for decades. If those frameworks are designed without input from the majority of the world's economies, they risk embedding assumptions that serve incumbent players and lock in advantages for early movers.

We have seen this pattern before. Internet governance debates in the 2000s were dominated by U.S. and European stakeholders, and the resulting architecture - from content moderation norms to data localization disputes - still reflects that imbalance. Efforts to broaden participation in bodies like ICANN and the Internet Governance Forum came late and have struggled to shift entrenched power dynamics.

AI governance is at an earlier stage, which means there is still time to avoid repeating that mistake. But the window is closing. Major regulatory frameworks are already being drafted in Brussels, Washington, and Beijing. Industry coalitions are forming around shared principles. Research consortia are setting evaluation protocols. Each of these initiatives claims to operate in the global interest, but the composition of their leadership and advisory boards tells a different story.

Building Inclusive Institutions

What would genuinely inclusive AI governance look like? It would start with representation: ensuring that standards bodies, research collaborations, and policy forums include voices from every region that will be affected by the technology. That means not just inviting token representatives, but providing the resources - travel funding, translation services, technical support - that allow meaningful participation.

It would also require humility about the limits of any single model. The assumption that a framework designed in one context can be exported wholesale to another is a form of technocratic hubris. Effective governance will be pluralistic, allowing for regional variation while maintaining interoperability where it matters - safety standards, for instance, or protocols for cross-border data flows.

Capacity-building is another piece. Many governments in the Global South lack the technical expertise to regulate AI effectively, not because their civil servants are less capable, but because they have fewer resources and less access to the talent pool. International institutions - the OECD, the UN, regional development banks - could play a role here, funding training programs, supporting local research networks, and facilitating knowledge exchange. So far, those efforts have been modest relative to the scale of the challenge.

Finally, funding models need to shift. The concentration of compute and capital in a few hands creates a bottleneck. Open-source models and distributed compute initiatives can help, but they require sustained investment. Philanthropies, development banks, and governments outside the traditional AI hubs should be exploring partnerships that decentralize access to frontier tools and datasets.

The Cost of Inaction

The Stanford letter is right: the stakes are enormous, and the timeline is short. But acting now means acting inclusively. If the institutions that govern AI are built without the participation of the Global South, they will lack legitimacy, they will fail to account for the conditions under which most people encounter the technology, and they will entrench inequalities that are already widening.

At DailyTechWire, we have watched venture capital flow into AI startups across Southeast Asia, India, and Latin America even as regulatory clarity remains elusive. Founders in those markets are building products for local users, but the rules they will eventually have to follow are being written elsewhere. That mismatch creates uncertainty, stifles innovation, and hands an advantage to incumbents with the resources to navigate fragmented regimes.

The next Industrial Revolution, if that is what this is, will not be contained within the borders of the countries that invented the steam engine. It will reshape work, wealth, and power everywhere. The governance structures we build now will determine whether that transformation is broadly shared or narrowly captured. Ensuring that the conversation includes the voices of the majority of the world is not a matter of fairness - it is a matter of getting the answer right.