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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

journal N3 2025

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:

  • Direct effects: automation and productivity gains.
  • Indirect effects: sectoral innovation and new markets.
  • Structural effects: shifts in labor markets, investment concentration, and trade disruptions.

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:

  • Ubiquitous broadband and 5G coverage.
  • Robust cloud infrastructure.
  • Ample R&D funding and skilled labor.
  • National AI strategies and legal frameworks.

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:

  • Digital Infrastructure: Urban areas have decent internet access, but rural connectivity lags. Georgia relies on international cloud services due to limited local data centers.
  • Human Capital: While the country produces IT graduates, very few specialize in AI. Brain drain is significant, and curricula often lack practical components.
  • Investment Constraints: R&D spending is only ~0.3% of GDP, far below global averages. Venture capital for tech is scarce and mostly donor-driven.
  • Policy Vacuum: As of 2025, Georgia lacks a national AI strategy. Most regulatory structures are adapted from the EU but lack local tailoring.

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

  1. Infrastructure: Developed countries have AI-ready infrastructure; Georgia has patchy, urban-centric systems.
  2. Human Capital: The developed world retains skilled AI talent; Georgia suffers from emigration and a lack of advanced training programs.
  3. Investment Capacity: Wealthier nations enjoy strong VC ecosystems; Georgia relies on foreign grants and has minimal domestic R&D.
  4. Policy Frameworks: Developed countries have mature AI policies; Georgia’s AI governance is still nascent.
  5. Industrial Readiness: Developed industries are digitalized and AI-integrated; Georgia’s economy remains dominated by low-tech SMEs and agriculture.

6. Policy Recommendations

For Developed Countries:

  • Share AI knowledge and technology with developing nations.
  • Ensure ethical AI standards and global cooperation.
  • Support capacity-building in less-developed markets via funding and partnerships.

For Developing Countries (like Georgia):

  • Develop national AI strategies aligned with local needs.
  • Invest in education and upskilling for digital and AI skills.
  • Build innovation ecosystems through public-private R&D labs.
  • Engage in regional and international alliances for knowledge exchange. 

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.