World Bank Advises Emerging Economies to Focus on Localized, Low-Cost AI
The World Bank released a report urging developing nations to quickly adopt AI for governance and business by utilizing localized, low-cost AI tools. The report advises these countries against trying to compete with wealthy nations in building massive data centers and large language models (LLMs). Adopting cost-effective AI could help developing nations overcome stagnant growth and accelerate progress in healthcare, education, and agriculture. However, failing to address basic infrastructure deficits like electricity and internet access could widen the productivity gap with wealthy nations. While high-income countries face three times the automation risk of low-income nations, developing countries face severe barriers, such as 30% of rural schools in Sub-Saharan Africa lacking stable electricity. The report warns that AI could reduce jobs in call centers and basic software services while increasing dependence on foreign technology.
## BACKGROUND
Large Language Models (LLMs) require massive computational resources and data centers to train and run. In contrast, Small Language Models (SLMs) use fewer parameters and lower arithmetic precision, making them cheaper to deploy on resource-constrained edge devices or single computers while still performing specific tasks effectively.