07/27/2026
The benefits of Large Language Models (LLMs) are not evenly distributed. For many in lower-middle-income countries, structural constraints limit their ability to take full advantage of these tools. Four foundational factors โ connectivity, compute, context, and competency โ continue to shape what is possible.
1๏ธโฃ Connectivity and infrastructure: Reliable internet, electricity, and access to digital devices remain uneven. Without these basics, the most advanced tools remain out of reach for large segments of the population.
2๏ธโฃ Compute and affordability: Accessing powerful models often requires cloud services or APIs that can be expensive. While smaller models are emerging, affordability and scalability remain concerns, particularly for governments and educational institutions working with limited budgets.
3๏ธโฃ Context and local data: LLMs are only as useful as the data they are trained on. Many low- and middle-income countries have rich but underutilized datasetsโoften fragmented and undigitized without adequate governance. Without stronger data ecosystems, AI tools risk reflecting global biases rather than local realities.
4๏ธโฃ Skills and trust: While LLMs lower entry barriers, they do not eliminate the need for skills. Users still need to interpret outputs, identify errors, and recognize bias. In many contexts, limited data literacy can lead to either over-reliance on AI or reluctance to use it altogether.
There is a real risk that LLMs could widen, rather than close, existing gaps. Countries and institutions with strong infrastructure, quality data, and skilled workforces will adopt these tools faster and more effectively. Others may lag not because of lack of interest, but because of systemic constraints. This makes policy choices critical. So, how do we harness the potential of LLMs as an equalizer, a proactive and coordinated effort?
โค First of all, by investing in foundations. This is to expand broadband access, improve energy reliability, and ensure access to affordable digital devices remain essential. These are not just infrastructure investmentsโthey are prerequisites for participation in the data economy.
โค Second, by expanding access to compute. Policies that reduce the cost of cloud services or support shared infrastructure can help widen access. Exploring โsmall AIโ solutions that run locally on devices may also offer new pathways.
โค Third, we need to strengthen data ecosystems. Countries need to invest in the digitization, curation, and governance of their own data. High-quality, locally relevant datasets are essential for building AI systems that reflect local needs, languages, and priorities.
โค Fourth, there is a need to build critical skills. AI and data literacy should go beyond basic familiarity. Users need to understand how to question outputs, assess reliability, and apply statistical reasoning. Training programs at all levels must evolve accordingly.
โค And last, but not least, we need to align policy and partnerships. National AI strategies, backed by clear implementation plans and partnerships across government, academia, and the private sector, can help coordinate efforts and avoid fragmentation.
