The Role of Artificial Intelligence in the Transformations of the International Economy

Document Type : Research

Authors

1 PhD graduate in International Relations, Faculty of Law and Political Science, Allameh Tabataba’i University, Tehran, Iran.

2 Full Professor of International Relations, Faculty of Law and Political Science, Allameh Tabataba’i University, Tehran, Iran.

10.22126/ipes.2026.9995.1634

Abstract

Artificial Intelligence (AI) has evolved from an aspirational concept into a transformative technological paradigm that is reshaping global economic structures. This study investigates the multifaceted impacts of AI on international economic dynamics, emphasizing its influence on productivity, labor markets, trade systems, institutional governance, and the global distribution of power. Grounded in the Endogenous Growth Theory, the paper adopts an analytical-descriptive methodology based on extensive library and documentary research. The findings reveal that AI functions as both an enabler and a disruptor: it enhances efficiency and innovation while simultaneously generating new inequalities and institutional challenges. Specifically, AI drives structural shifts in five domains—international trade and development, institutional adaptation, productivity and efficiency, employment transformation, and power redistribution. Ultimately, the study concludes that AI is not merely a technological innovation but a systemic force capable of redefining economic interactions, governance frameworks, and competitive hierarchies at the global level.
Over the past decade, AI has emerged as one of the most influential determinants of global economic transformation. Its capacity for autonomous decision-making, predictive analysis, and large-scale automation has redefined how production, trade, and finance operate across borders. Scholars increasingly argue that AI represents the “engine of growth” in the 21st-century international economy, shaping not only business models but also geopolitical and institutional configurations.
This paper seeks to address a central question: How does the technological evolution of artificial intelligence alter the structure and dynamics of the international economy? By situating this question within the broader discourse on globalization and technological innovation, the study explores how AI-driven processes affect employment, trade flows, governance mechanisms, and power distribution among nations.

Theoretical Framework

The study employs Paul Romer’s Endogenous Growth Theory (1990) as its conceptual foundation. Unlike exogenous growth models that attribute development to external inputs, this theory posits that innovation, knowledge, and human capital are internal engines of economic expansion. Within this framework, AI acts as an endogenous technological driver that accelerates innovation cycles, enhances labor productivity, and fosters knowledge diffusion.
By augmenting research and development (R&D), AI strengthens human capital formation and creates increasing returns to scale, leading to sustainable economic growth. Moreover, AI’s analytical and computational capacities enable economies to internalize knowledge creation, making it a fundamental mechanism for continuous technological advancement and global competitiveness.

Methodology

 This research adopts a qualitative, analytical-descriptive approach. Data were collected from a broad range of sources, including peer-reviewed journal articles, policy papers, institutional reports, and international databases. The analysis integrates comparative evaluation of global investment trends in AI, adoption rates across industries, and labor market shifts between 2013 and 2022. The study emphasizes conceptual interpretation and synthesis over statistical quantification, focusing on the causal relationships between AI diffusion and macroeconomic transformation in the global context.

Results & Discussion

4.1. AI and Global Trade Development
AI optimizes logistics, supply chains, and customs management, reducing transaction costs and enhancing trade efficiency. Machine translation, predictive analytics, and algorithmic forecasting facilitate cross-border business and international market integration. However, the so-called “modern productivity paradox” suggests that the measurable macroeconomic benefits of AI adoption may lag behind its rapid technological diffusion. 4.2. Institutional and Governance Transformation: AI challenges traditional institutional frameworks and necessitates adaptive regulatory architectures. Emerging global initiatives—such as the European Union’s Artificial Intelligence Act (2021)—illustrate the need for ethical, transparent, and harmonized governance systems. The rise of “machine economies” also calls for redefined norms regarding data ownership, algorithmic accountability, and cross-border cooperation. 4.3. Redistribution of Economic Power: AI intensifies global economic competition by amplifying disparities between technologically advanced and lagging nations. The United States and China have emerged as dominant AI superpowers, leveraging technological leadership to extend their geopolitical influence. This dynamic fosters a new form of digital mercantilism, wherein technological capability becomes a determinant of economic sovereignty. 4.4. Productivity and Efficiency Gains: Empirical evidence indicates that AI adoption in manufacturing, logistics, and services leads to substantial cost reduction and performance improvement. Predictive maintenance, intelligent automation, and data-driven decision-making contribute to significant increases in productivity growth and operational scalability, potentially adding trillions of dollars to global GDP by 2030. 4.5. Employment and Labor Market Transformation: AI’s dual impact on labor markets involves both displacement and creation. Routine and repetitive jobs are increasingly automated, while new, high-skill occupations emerge in data analytics, algorithm design, and AI system management. The long-term employment effects depend on workers’ adaptability and the effectiveness of educational and reskilling programs supported by public policy.

Conclusions & Suggestions

 Artificial Intelligence represents a pivotal inflection point in the evolution of the international economy. It simultaneously enhances productivity and disrupts traditional market structures, redefining trade patterns, labor relations, and institutional governance. While AI-driven automation raises concerns about inequality and job displacement, it also generates unprecedented opportunities for innovation and sustainable growth. The study concludes that the future of the international economic order will depend on how effectively nations integrate AI into their development strategies while mitigating its socio-economic risks. Balanced governance, inclusive education policies, and cooperative international regulation are essential for ensuring that the benefits of AI-driven transformation are equitably distributed across the global economy.
Ethical Considerations
Not applicable
Funding
Not applicable
Conflict of interest
The authors declare no conflict of interest

Keywords

Main Subjects

References

Acemoglu, D., & Restrepo, P. (2017). The Race between Machine and Man: Implications of Technology for Growth, Factor Shares and Employment (NBER Working Paper No. 22252). National Bureau of Economic Research. doi: 10.3386/w22252
Bellman, R. E. (1978). Artificial intelligence: Can computers think? CiNii Books.
Bertram, J. S., & Craig, A. W. (1972). Specific induction of bladder cancer in mice by butyl-(4-hydroxybutyl)-nitrosamine and the effects of hormonal modifications on the sex difference in response. European Journal of Cancer, 8(6), 587–594. doi: 10.1016/0014-2964(72)90137-5
Bessen, J., Goos, M., Salomons, A., & van den Berge, W. (2020). Automation: A Guide for Policymakers. Economic Studies at Brookings Institution.
Brynjolfsson, E., & McAfee, A. (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W. W. Norton & Company.
Brynjolfsson, E., Hui, X., & Liu, M. (2018). Does machine translation affect international trade? Evidence from a large digital platform. National Bureau of Economic Research Working Paper. doi: 10.3386/w24917
Charniak, E. (1985). Introduction to Artificial Intelligence. Pearson Education India.
Chen, N., Christensen, L., Gallagher, K., Mate, R., & Rafert, G. (2016). Global Economic Impacts Associated with Artificial Intelligence. Analysis Group. http://www. analysisgroup.com/uploadedfiles/content/insights/publishing/ag_full_report_economic_impact_of_ai.pdf
Cihon, P. (2019). Standards for AI Governance: International Standards to Enable Global Coordination in AI Research and Development. Future of Humanity Institute, University of Oxford. https://www.fhi.ox.ac.uk/wp-content/uploads/Standards_FHI_Technical_ Report.pdf
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20, 273–297. doi: 10.1007/BF00994018
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009). ImageNet: A large-scale hierarchical image database. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 248–255). IEEE. doi: 10.1109/CVPR.2009. 5206848
European Commission (2021). Proposal for a Regulation of the European Parliament and of the Council Laying Down Harmonised Rules on Artificial Intelligence (Artificial Intelligence Act) and Amending Certain Union Legislative Acts (COM/2021/206 final). European Commission. https://eur-lex.europa.eu/legal-content/EN/ALL/?uri=CELLAR: e0649735-a372-11eb-9585-01aa75ed71a1
European Parliamentary Research Service (2019). Economic Impacts of Artificial Intelligence (AI).
Ferrucci, D., Levas, A., Bagchi, S., Gondek, D., & Mueller, E. T. (2013). Watson: Beyond Jeopardy! Artificial Intelligence, 199, 93–105. doi: 10.1016/j.artint.2012.06.009
Furubotn, E. G., & Richter, R. (2005). Institutions and Economic Theory: The Contribution of the New Institutional Economics. University of Michigan Press
Golafshan, A., & Abbasiashlaghi, M. (2025). Patterns of China's political economy in West Asia; A case study of Saudi Arabia (1991-2023). International Political Economy Studies, 8(1), 27-48. doi: 10.22126/ipes.2025.10994.1682 (In Persian).
Goodfellow, I. J., Vinyals, O., & Saxe, A. M. (2014). Qualitatively characterizing neural network optimization problems. arXiv Preprint arXiv:1412.6544.
Harari, Y. N. (2016). Homo Deus: A Brief History of Tomorrow. Harvill Secker.
Hassani, H., Silva, E. S., Unger, S., TajMazinani, M., & Mac Feely, S. (2020). Artificial intelligence (AI) or intelligence augmentation (IA): What is the future? AI, 1(2), 8. doi: 10.3390/ai1020008
Haugeland, J. (1989). Artificial intelligence: The very idea. MIT Press.
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. doi: 10.1162/neco.1997.9.8.1735
Kenny, D. (2022). Machine translation for everyone: Empowering users in the age of artificial intelligence. Language Science Press.
Krizhevsky, A. (2012). AlexNet: Convolutional neural networks.
Kurzweil, R., Richter, R., Kurzweil, R., & Schneider, M. L. (1990). The age of intelligent machines (Vol. 580). MIT Press.
LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2324. doi: 10.1109/5.726791
Le-Qing, Z., & Zhen, Z. (2012). Automatic insect classification based on local mean colour feature and support vector machines. Oriental Insects, 46(3–4), 260–269. doi: 10.1080/00305316.2012.738142
Lowndes, V., & Roberts, M. (2013). Why institutions matter: The new institutionalism in political science. Macmillan International Higher Education.
Ma, B., Nahal, S., & Tran, F. (2015). Robot revolution: Global robot & AI primer. Bank of America Merrill Lynch.
Mandel, M. (2017). How e-commerce creates jobs and reduces income inequality. Progressive Policy Institute. http://www.progressivepolicy.org/wp-content/uploads/2017/09/PPI_ ECommerceInequality_final.pdf
Manyika, J., Lund, S., Chui, M., Bughin, J., Woetzel, J., Batra, P., ... & Sanghvi, S. (2017). Jobs lost, jobs gained: Workforce transitions in a time of automation. McKinsey Global Institute.
Meltzer, J. (2018). The impact of artificial intelligence on international trade. https://www. brookings.edu/research/the-impact-of-artificial-intelligence-on-international-trade
Michie, D. (1963). Experiments on the mechanization of game-learning. Part I: Characterization of the model and its parameters. The Computer Journal, 6(3), 232–236.
Minsky, M., & Papert, S. A. (1969). Perceptrons. MIT Press.
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., & Hassabis, D. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529–533. doi: 10.1038/nature14236
Moravec, H. P. (1983). The Stanford cart and the CMU rover. Proceedings of the IEEE, 71(7), 872–884.
Newell, A. (1962). Some problems of basic organization in problem-solving programs (pp. 393–423). Rand Corporation.
Nilsson, N. J. (1998). Artificial intelligence: A new synthesis. Morgan Kaufmann.
Ogheneovo, E., & Nlerum, P. (2020). Iterative dichotomizer 3 (ID3) decision tree: A machine learning algorithm for data classification and predictive analysis. International Journal of Advanced Engineering Research and Science, 7, 514–521.
Pachocki, J., Roditty, L., Sidford, A., Tov, R., & Williams, V. (2018). Approximating cycles in directed graphs: Fast algorithms for girth and roundtrip spanners. In Proceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete Algorithms (pp. 1374–1392). Society for Industrial and Applied Mathematics.
Poole, D. (1998). Decision theory, the situation calculus and conditional plans. Linköping University Electronic Press.
Rich, E., & Knight, K. (1991). Artificial intelligence. McGraw-Hill.
Romer, P. M. (1990). Endogenous technological change. Journal of Political Economy, 98(5), S71–S102.
Rosenblatt, F. (1961). Statute: The program is concerned equally with biological and engineering. Current Research and Development in Scientific Documentation, 9, 212.
Russell, S., & Norvig, P. (2016). Artificial intelligence: A modern approach (3rd ed.). Pearson Education Limited.
Salimi, H., & Imani, H. (2025). Neoliberal Approach in Global Political Economy: Transition from State-Centric to Networked World. International Political Economy Studies, 8(1), 1-26. doi: 10.22126/ipes.2025.11338.1708 (In Persian).
Samoili, S., Lopez Cobo, M., Gomez Gutierrez, E., De Prato, G., Martinez-Plumed, F., & Delipetrev, B. (2020). AI watch: Defining artificial intelligence (EUR 30117 EN, JRC118163). Publications Office of the European Union. doi: 10.2760/382730
Samuel, A. L. (1960). Programming computers to play games. In Advances in Computers (Vol. 1, pp. 165–192). Elsevier.
Schmidhuber, J. (1993). A self-referential weight matrix. In Proceedings of the International Conference on Artificial Neural Networks (ICANN'93) (Vol. 3, pp. 446–450). Springer.
Shapiro, C., & Varian, H. R. (2018). Information rules: A strategic guide to the network economy. Harvard Business School Press.
Sharma, M., Luthra, S., Joshi, S., & Kumar, A. (2022). Implementing challenges of artificial intelligence: Evidence from public manufacturing sector of an emerging economy. Government Information Quarterly, 39(4), Article 101624. doi: 10.1016/j.giq.2022. 101624
Shortliffe, E. H., Davis, R., Axline, S. G., Buchanan, B. G., Green, C. C., & Cohen, S. N. (1975). Computer-based consultations in clinical therapeutics: Explanation and rule acquisition capabilities of the MYCIN system. Computers and Biomedical Research, 8(4), 303–320.
Smuha, N. A. (2021). From a race to AI to a race to AI regulation: Regulatory competition for artificial intelligence. Law, Innovation and Technology, 13(1), 57–84. doi: 10.1080/17579961.2021.1872783
Stix, C. (2022). Foundations for the future: Institution building for the purpose of artificial intelligence governance. AI and Ethics, 2(3), 463–476. doi: 10.1007/s43681-022-00186-6
Tesauro, G. (2002). Programming backgammon using self-teaching neural nets. Artificial Intelligence, 134(1–2), 181–199.
The Economist. (2017, April 12). Automatic for the people: How Germany's Otto uses artificial intelligence. https://www.economist.com/news/business/21720675-firm-using-algorithm-designed-cern-laboratory-how-germanysotto-uses
Thrun, S., Montemerlo, M., Dahlkamp, H., Stavens, D., Aron, A., Diebel, J., ... & Mahoney, P. (2006). Stanley: The robot that won the DARPA Grand Challenge. Journal of Field Robotics, 23(9), 661–692.
Tsolakis, N. (2021). Towards AI driven environmental sustainability: An application of automated logistics in container port terminals. International Journal of Production Research, 1–21. doi: 10.1080/00207543.2021.1914355
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
Vinyals, O., Babuschkin, I., Czarnecki, W. M., Mathieu, M., Dudzik, A., Chung, J., ... & Silver, D. (2019). Grandmaster level in StarCraft II using multi-agent reinforcement learning. Nature, 575(7782), 350–354. doi: 10.1038/s41586-019-1724-z
Wagner, D. N. (2020). Economic patterns in a world with artificial intelligence. Evolutionary and Institutional Economics Review, 17(1), 111–131.
Watkins, C. J. C. H. (1989). Learning from delayed rewards. King's College, University of Cambridge.
Weizenbaum, J. (1966). ELIZA—A computer program for the study of natural language communication between man and machine. Communications of the ACM, 9(1), 36–45.
Winograd, T. (1971). Procedures as a representation for data in a computer program for understanding natural language. Artificial Intelligence Laboratory Technical Report (AITR-235), Stanford University. http://hci.stanford.edu/winograd/shrdlu/AITR-235.pdf
Winston, P. H. (1992). Artificial intelligence. Addison-Wesley.
Wolff, J., Pauling, J., Keck, A., & Baumbach, J. (2020). The economic impact of artificial intelligence in health care.
World Customs Organization. (2019). Study report on disruptive technologies. http://www.wcoomd.org/-/media/wco/public/global/pdf/topics/facilitation/instruments-and-tools/tools/disruptive-technologies/wco_disruptive_technologies_en.pdf?la=en
International Political Economy Studies
Volume 9, Issue 1
May 2026
Pages 1-24

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History

  • Receive Date: 13 December 2023
  • Revise Date: 14 January 2026
  • Accept Date: 23 March 2026

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