Executive Summary
Artificial
intelligence has moved from experimental technology to a general-purpose driver
of economic transformation, and development economics is being rewritten as a
result. Global corporate AI investment reached $252.3 billion in 2024 and, per
Stanford's 2026 AI Index, surged to $581.7 billion in 2025 — yet more than
three-quarters of that private capital remains concentrated in the United
States a
lone. This asymmetry sits atop deeper divides: 2.2 billion people
remain offline globally, five-sixths of them in low- and middle-income
countries, and only 23% of people in low-income countries use the internet
compared with 94% in high-income economies. At the same time, IMF and ILO
research shows AI's labour-market exposure is lower in poorer countries (around
26–28% versus 60% in advanced economies) — a "double-edged" finding,
since lower exposure also signals weaker readiness to capture AI's productivity
dividend. This article examines how development economics is evolving in
response: from digital public infrastructure in India and Estonia to AI-enabled
health logistics in Rwanda, precision agriculture, and algorithmic public administration.
It argues that the decisive variable is not technological access but
institutional capability — governance, human capital, data infrastructure, and
regulatory maturity. Without deliberate policy design, AI risks entrenching a
new hierarchy of nations; with it, AI can become one of the most powerful
accelerants of inclusive growth since the Green Revolution. The article closes
with concrete recommendations for governments, development banks, the private
sector, and universities.
