Building AI Without Breaking the Bank
Rethinking AI for the Global South
AI has the potential to transform health care, agriculture, and governance in developing countries, but misconceptions about high costs often stifle progress. Many assume success requires massive investments in proprietary infrastructure and cutting-edge tech, but recent examples show low-cost, high-impact AI projects already thriving across the Global South. From disease surveillance in Uganda to local crop prediction models in India, these initiatives leverage open-source tools, regional data, and context-driven innovation rather than billion-dollar budgets. Experts argue that by focusing on human capital and community-specific needs, developing nations can establish their own AI paths that are not merely replicas of Silicon Valley’s model but reflect local priorities and capabilities.
People, Not Products
The conversation around AI’s future in low-income countries must shift from tech acquisition to talent cultivation. Investing in local education, research partnerships, and data sovereignty is proving more sustainable than importing expensive black-box systems. This approach fosters innovation ecosystems rooted in local expertise and problems—not in profit-driven motives of global tech giants. Countries like Rwanda and Bangladesh are already showing how grassroots data collection and localized algorithm development can improve public services at scale. The key lies in empowering people to shape AI, rather than being shaped by it.
