AI Diffusion to Low- and Middle Income Countries; A Blessing or a Curse?

TL;DR

This paper investigates how AI technologies spread across economies, especially in developing countries, highlighting the current gaps, key diffusion pathways, and policy implications for equitable AI-driven economic benefits.

Contribution

It introduces a tripartite framework for AI diffusion analysis and evaluates diffusion patterns in 16 LMICs compared to developed nations, emphasizing global value chains, research collaboration, and inter-firm knowledge transfer.

Findings

Significant AI diffusion gap between developed and developing countries, gradually closing.

China is a key future source of AI diffusion through value chains.

The USA influences AI diffusion mainly via research and knowledge transfer.

Abstract

Rapid advances in AI have incited extensive inquiry into its effects on productivity and labor, potentially profound in both positive and negative ways. Often neglected, however, is comprehension of how AI technologies diffuse across and within economies. Developing nations, in particular, face substantial labor market impacts from either swift AI adoption or diminished competitiveness from sluggish diffusion. This paper examines the literature on technology diffusion and proposes a tripartite framework to elucidate AI diffusion pathways: global value chains, research collaboration, and inter-firm knowledge transfers. Employing these metrics, it evaluates AI diffusion in sixteen lower- and middle-income countries (LMICs) relative to four developed nations and assesses their dependency on the USA and China. Findings reveal a notable gap in AI diffusion between developed and developing economies, though this chasm is gradually closing. China emerges as a vital source of future diffusion via value chains, while the USA wields greater influence through research and knowledge transfers. Limitations include the exclusion of certain data sources and regions, and the absence of quantitative analysis on diffusion's relationship with technology intensity. Nonetheless, the research surfaces critical macro-level considerations about AI diffusion. It advocates mechanisms to redistribute AI-induced economic gains and bilateral agreements to complement international accords, thereby addressing the diverse needs and risks of economies entering an AI-dominated era. Future inquiries should explore the nexus between AI diffusion, technology intensity, and productivity; refine diffusion measurement methods; incorporate case studies and targeted policy recommendations; and delve deeper into LMIC-specific labor market outcomes.