Where the Hype Don’t Shine: the promise and perils of AI for developing countries
In an interview with The Economic Times, a prestigious Indian newspaper, Microsoft CEO Satya Nadella asked about the distinction between what is a ‘developed’ and ‘developing’ country, of which the latter term faces controversy for its hierarchical implications globally: “At the end of the day, what’s the difference between being a developed country and a developing country? It’s just the rate of growth over long periods of time”. Mr. Nadella is, as Damien Vreznik of The Economist concluded, “haunted by the fact that the Industrial Revolution left behind India, his country of birth.” Indeed, many countries’ development spanning the first waves of industrialization to the rise of the internet lacked both participation and crucial investment until decades later. A similar pattern exists in the emergence of mass AI technology, with the West dominating much of the innovation and market share. Developing countries (or developing regions therein, such as India or Nigeria) appear slated once again to be passive recipients of this new technology, or the “fourth revolution” more broadly.
However, the nature of this technology is not akin to prior eras of mass innovation. Instead, the advent of AI may exist as the accelerant needed to lower traditionally steep barriers to entry for mass adoption and participation in development. AI-powered language models (LLMs) are advancing the democratization of information and learning, building upon the foundation laid by the internet by offering more personalized, interactive, and context-aware access to knowledge. Moreover, popular AI tools currently on the market claim to improve productivity within public information, financial and legal services, marketing and advertising, and software development. The AI ‘revolution’ is distinct from others in that the majority of its disruption has been across white-collar workers. According to the IMF, 30% of jobs in advanced economies are at risk for replacement by AI, compared to 20% in emerging markets and 18% in low-income countries.
Fortunately, the rate of expansion enabled by widespread internet access also separates the AI moment from the pack of innovation. As for ownership, model deployment is increasingly democratized, and the design of large model training accommodates localized data to fine-tune existing foundation models that originated in advanced tech markets. While compute and energy remain a necessity, developers no longer require scale-enabling resources to reinvent generative approaches according to their needs. To this end, an awakening of startups in India, Indonesia, Kenya, Nigeria, and elsewhere is helping to realize the potential of bespoke datasets to enrich models for their markets. As the cost of training AI models reduces (to the benefit of these nations), smaller and cheaper models in these countries can extend AI tools to domestic needs that would overlooked by big developers. For instance, agricultural monitoring models for smaller farmers with limited water supply and software knowledge; medical predictive models to detect region-specific diseases like malaria in sub-Saharan Africa; AI microfinance tools for largely unbanked populations; telemedicine AI to alleviate the strain on hospital; and freely-accessible educational apps for reskilling large youth populations dealing with teacher shortages. Past revolutions have disappointed many million members of nations who have stood to gain the most. While AI remains an ambiguous insurgence, there is, by its character, undeniable opportunity.
Further reading:
- AI holds tantalising promise for the emerging world
- Are Developing Countries Prepared for the Fourth Industrial Revolution?
- How AI could transform the lives of the world’s poorest
- Artificial intelligence (AI) can help developing economies diversify
- AI for Developing Countries Forum
originally published in July 2024 under the University of Cambridge newsletter “The Good Robot Podcast”
