The Global South is Building Its Own AI Brainpower

This summary is based on an article titled “As AI giants duel, the Global South builds its own brainpower,” published as part of a collection on artificial intelligence in the Global South created jointly by Nature India, Nature Africa, and Nature Middle East.

Summary: The Global South is Building Its Own AI Brainpower

The global AI landscape is shifting beyond the duel between giants in the United States and China. Researchers, engineers, and public institutions in the Global South, including countries across Africa, Asia, and the Middle East, are developing their own homegrown AI systems and language models. Their focus is less on building ever larger models and more on “scaling right” – creating models that work effectively for local users, in their specific languages, and within their unique social and economic realities.

The article highlights a key limitation of dominant AI models trained primarily on English data: they often struggle with languages from the Global South, not only in grammar and syntax but also in capturing crucial “cultural nuances, dialectal variations, and even non-standard scripts“. The meaning in many of these languages can lie heavily “in the implication” rather than just the explicit words, and if English data dominates training, the output may be “filtered through a Western lens rather than an Arab one“.

Building these local models faces significant challenges, particularly “data scarcity“. Even languages with large speaker populations may lack the extensive digital datasets available for languages like English, making training models from scratch difficult. Additionally, existing datasets are often imbalanced, favouring high-resource languages, which can introduce biases. Practical obstacles also include limited funding, patchy computing infrastructure, and a shortage of specialists.

Despite these hurdles, there is a strong movement to build AI that serves local needs and promotes “digital independence“. Governments in countries like Saudi Arabia, the UAE, Qatar, and India are investing resources into creating Arabic-first or Indic-language models. Concurrently, ground-up initiatives like Masakhane NLP and Deep Learning Indaba in Africa are fostering decentralized research to develop models for diverse African languages.

Ultimately, the vision emerging in the Global South is one where AI has a “practical impact over prestige, and community ownership over corporate secrecy“. The focus is on “solving real problems for real people,” such as developing tools for farmers, students, and small business owners in their native languages. These efforts represent a future where AI doesn’t just speak to people but speaks with them, particularly for the billions who speak the world’s less-resourced languages.

The findings of this article aligns with the plain X mission to support diverse language models and bridge the language gap for low-resource languages.

More information: https://www.nature.com/immersive/d44151-025-00085-3/index.html