Artificial Intelligence in Emerging Markets: Leapfrog or Lag?

The AI revolution is not evenly distributed. While developed markets lead in foundational model development, emerging markets have unique advantages in AI application and deployment.

Three factors support the leapfrog thesis: greenfield opportunity, as emerging markets can build AI-native systems without legacy technology debt; data abundance, with large populations generating vast training datasets; and necessity-driven innovation, where resource constraints drive creative AI applications.

We're seeing this play out across our portfolio. In healthcare, AI-powered diagnostics are extending specialist capabilities to underserved regions. In agriculture, computer vision and predictive analytics are transforming smallholder farming. In financial services, AI-driven underwriting is enabling credit access for the previously unbanked.

However, challenges remain. Talent concentration in developed markets, limited computational infrastructure, and evolving regulatory frameworks create headwinds. Success requires thoughtful approaches to each constraint.

Our investment strategy focuses on applied AI companies solving locally-relevant problems with globally-scalable technology. We believe the winners in emerging market AI will combine deep domain expertise with technical excellence and capital efficiency.