�鶹������ý

The conversation about artificial intelligence (AI) is dominated by a preoccupation with frontier capabilities, leaderboard performance, and ever-larger models. Yet a more consequential challenge facing the field is the gap between what AI can do and what it is actually doing in the world. This “” is global, but it is particularly acute in the Global South, where AI is being deployed amid resource constraints, limited state capacity, and fragile institutions.

This report sets out to understand AI adoption challenges in the Global South: their nature, their drivers, and the workarounds practitioners are already developing on the ground. Its distinguishing feature is empirical. Drawing on 14 interviews, six structured surveys, and a corpus of 53 case studies, the report builds its case through granular, ground-up evidence—in the process, pushing back against the abstraction that often dominates AI policy conversations.

The report is primarily descriptive rather than prescriptive, but four cross-cutting observations run through the evidence:

• First, AI adoption is fundamentally a problem of alignment with real-world systems, not model capability; contextual fit matters more than benchmark performance.
• Second, adoption is a whole-of-society challenge that cannot be resolved at the technical layer alone.
• Third, constraint is generative as well as limiting: resource scarcity is producing distinctive forms of innovation—frugal engineering, modular compute, edge deployment, participatory data collection—that may have relevance well beyond the Global South.
• Fourth, adoption gaps are unevenly distributed and tend to compound for already excluded populations, particularly women.

The report examines eight parameters that shape AI adoption: four technical (compute, models, data, and infrastructure), and four nontechnical (social and institutional embeddedness, capacity and skills, trust, and funding). For each parameter, the report documents both challenges and—importantly—emerging responses and workarounds. Documenting responses alongside challenges is essential: It surfaces on-the-ground innovations that are already taking shape, and points to where policymakers and funders can most usefully build.

The empirical evidence yields a number of findings, many of which go against conventional wisdom or dominant narratives. Here is a partial list:

• The conversation over compute has the wrong unit. Most Global South deployments fine-tune or run inference rather than train frontier models; their compute needs look almost nothing like the gigawatt headline numbers that dominate conversations in the Global North.
• Open source needs a deployment ecosystem, not just a model. Grassroots organizations still often rely on commercial U.S. providers because open-source alternatives lack the technical assistance and onboarding support that make commercial models easy to adopt.
• AI sovereignty laws can undermine the deployments they are meant to protect. While generally well-intended, data and cloud localization rules often prevent domestic start-ups from accessing the compute they need to successfully deploy AI.
• Embed AI in platforms and workflows users already trust. The friction of asking users to adopt new workflows routinely defeats otherwise capable AI tools; WhatsApp integration in particular consistently drives adoption.
• Voice and image interfaces are essential—but poor connectivity can defeat them. Voice and multimodal input are often the best way to reach low-literacy and digitally excluded users, but the communities that would benefit most often have the weakest connectivity.
• DPI stacks are an underused source of AI-ready data, but require careful governance. Where digital public infrastructure exists, it can produce structured, AI-ready data at scale—but only if governance frameworks for consent and reuse are in place.
• Community data collection is reshaping how AI is built in the Global South. The most innovative approaches go beyond labels to capture idioms and cultural meaning embedded in the data. Royalty and licensing models offer a way to compensate contributors as ongoing partners and create recurring sources of revenue.
• Benchmarking is a hidden source of bias. Even models built on well-collected local data can be misjudged if the benchmarks used to evaluate them contain Global North or other cultural assumptions.

Together, the findings gesture toward the contours of a distinct approach to AI development: one that is frugal rather than capital-intensive, embedded rather than imposed, and attentive to gaps and inequalities. If it coheres, this could emerge as a genuine alternative to today’s dominant paradigms—a model of AI development built for, and from, the Global South. The grassroots deployment work documented in this report, often unrecognized and unsupported, suggests that the building has already begun. Now the institutional, funding, and technical ecosystem needs to catch up.