![]() EKONOMISTI
The international scientific and analytical, reviewed, printing and electronic journal of Paata Gugushvili Institute of Economics of Ivane Javakhishvili Tbilisi State University ![]() |
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Journal number 3 ∘
Nikoloz Javakhishvili ∘
Developed and Developing Economies in the Age of Artificial Intelligence: A Comparative Analysis of Economic Transformation 1. Introduction and Thesis AI has become a transformative force in the global economy, improving productivity, altering labor dynamics, and enabling new business models. However, this impact is not uniform across countries. Developed economies—particularly in North America and Europe—have advanced AI ecosystems, while many developing countries face foundational challenges. The article argues that the divergence stems from structural readiness: the presence or absence of digital infrastructure, human capital, investment capacity, and governance frameworks. By comparing developed countries with Georgia, the article shows how these structural factors mediate the depth and direction of AI’s economic influence. 2. Conceptual Framework AI is defined in terms of three types: narrow AI (task-specific), general AI (hypothetical, human-level), and generative AI (which creates content like text or images). Today’s real-world applications fall under narrow and generative AI, which are increasingly seen as General-Purpose Technologies (GPTs), akin to electricity or the internet. Three pathways summarize AI’s economic influence:
These dynamics unfold differently in countries with varying readiness levels. 3. Developed Economies: Trends and Outcomes In North America and Western Europe, AI is widely adopted across industries such as finance, healthcare, logistics, and manufacturing. These regions benefit from:
These conditions have resulted in measurable productivity growth. For example, McKinsey and OECD estimate that AI could raise GDP growth in these economies by 1–2% annually by 2030. Labor markets are also transforming: routine jobs are declining, while demand for high-skill digital roles is rising. Policies in these countries tend to support AI adoption while addressing ethical, legal, and social impacts. Education systems emphasize digital literacy, and regulatory frameworks ensure accountability in AI applications. 4. Developing Economies and the Case of Georgia Georgia, while classified as an upper-middle-income country with a high HDI, represents the typical challenges and opportunities facing developing countries in the AI era. Key observations include:
Nonetheless, Georgia has made strides in e-governance, launching digital public services and supporting startups. Programs like Startup Georgia and support from partners like the EU and UNDP have helped, but scale and impact remain limited. 5. Comparative Analysis: Five Key Differences
6. Policy Recommendations For Developed Countries:
For Developing Countries (like Georgia):
Leapfrogging is a viable strategy—using AI to skip developmental stages in sectors like banking, education, and agriculture. Georgia, for instance, could adopt AI-powered fintech or AI in precision farming without legacy infrastructure constraints. 7. Conclusion AI has the potential to reinforce global inequality unless proactive steps are taken. Developed countries lead due to structural advantages, but developing countries like Georgia can still benefit through strategic intervention. The path forward must include investment in people, infrastructure, and governance, backed by global cooperation. The AI revolution must be guided to serve all economies—not just those already ahead. This summary highlights the central findings and recommendations of the original academic article while maintaining analytical depth and structural clarity. |