AI Strategy in Developing Economies

2025-10-04 - 3 minutes read

Sophie Zhu-Yun Pitt and Taylor Reynolds
Oct 3, 2025

As the global economy is reshaped by Artificial Intelligence, developing countries are actively seeking ways to harness its benefits despite often significant resource constraints. They face the challenge of doing so with limited resources and a lack of foundational infrastructure.

In this new working paper, we re-evaluated the standard “factors of production”—Land, Labor, Capital, and Entrepreneurship—through an AI-specific lens to identify strategic areas for AI investment in developing countries.

• Land becomes unique, context-rich local datasets.
• Labor encompasses human capital, especially a scalable workforce across the AI supply chain.
• Capital is includes computational resources and the strategic use of open-source tools.
• Entrepreneurship is the capacity for innovation driven by a deep understanding of local needs.

Key Findings on Comparative Advantages

Our analysis reveals that while developing countries face disadvantages in capital, they possess distinct strengths in other areas.

Land (Data): The research finds that a significant advantage lies in unique local datasets (e.g., local agricultural patterns, linguistic nuances, public health records). This data is invaluable for training AI models to solve specific local problems that global models overlook.

Labor: The paper highlights the advantage of large, adaptable workforces ideal for the essential, labor-intensive tasks of data annotation and human-in-the-loop (HITL) processes. This provides a direct entry into the AI value chain and a pathway for upskilling.

Capital: Given that building large-scale infrastructure is often prohibitive, the research determines the best path forward is a capital-light approach. By leveraging open-source models, APIs, and cloud computing, nations can bypass immense hardware costs and focus on developing value-added applications.

Entrepreneurship: The analysis identifies a culture of necessity-driven innovation as a core strength. Local entrepreneurs are uniquely positioned to apply AI to solve pressing, on-the-ground challenges in sectors like healthcare, education, and finance.

Policy Recommendations

Based on this analysis, we propose a set of concrete policy directions:

1. Launch national data curation initiatives to build high-quality, locally relevant datasets.
2. Implement a tiered AI education strategy to build skills from basic digital literacy to applied AI development.
3. Foster an ecosystem for AI entrepreneurship with seed funding, incubators, and regulatory sandboxes.
4. Invest in foundational digital infrastructure and pursue regional collaborations to build shared data centers.

The research shows that most effective AI strategy for developing countries is not be to imitate the capital-intensive models of advanced economies, but to strategically leverage their own unique advantages. This approach allows nations to actively shape their digital future and apply AI to help address some of the most pressing development challenges.

The full paper and methodology are available here.