ISSN (print) - 0131-7652

ISSN (online) - 2686-7605

Vol. 56 No. 2 (2026)
Cover story: “Almighty AI“?

The Dilemma Facing the Artificial Intelligence Industry: Balancing Technological Breakthroughs with Threats to Stability

Requires Subscription or Fee PDF (Русский) (RUB 200)
  • L.V. Matraeva,
  • E.S. Vasiutina
L.V. Matraeva
Russian Technological University
E.S. Vasiutina
Russian Technological University
DOI: https://doi.org/10.30680/ECO0131-7652-2026-2-7-25

Published 2026-04-06

Keywords

  • Artificial intelligence; risks; AI regulation; economic and social impact of AI; systemic threats; cybersecurity

How to Cite

1.
Matraeva Л, Vasiutina Е. The Dilemma Facing the Artificial Intelligence Industry: Balancing Technological Breakthroughs with Threats to Stability. ECO [Internet]. 2026 Apr. 6 [cited 2026 Apr. 7];56(2):7-25. Available from: https://ecotrends.ru/index.php/eco/article/view/4951

Abstract

This paper is devoted to the analysis and systematization of risks accompanying the development of the artificial intelligence (AI) industry. Based on theoretical principles of technological development and concepts of digital regulation, four key risk groups affecting the stability of macroeconomic and social systems have been identified: labor market transformation, systemic distortions of efficiency, threats to data reliability, and hidden social consequences. The authors present their vision of a responsible AI model that can serve as a basis for developing a regulatory framework and mechanisms for supervising the use of AI technologies in order to minimize these risks.
Requires Subscription or Fee PDF (Русский) (RUB 200)

References

  1. Кочергин С.В., Артемова С.В., Бакаев А.А., Митяков Е.С., Вегера Ж.Г., Максимова Е.А. Кибербезопасность смарт-сетей: сравнение подходов машинного обучения для обнаружения аномалий // Russian Technological Journal. 2024. № 12(6). С. 7–19. DOI: 10.32362/2500–316X-2024–12–6–7–19
  2. Пашенцев Е.Н. Искусственный интеллект: новые угрозы для международной информационно-психологической безопасности и пути их нейтрализации // Новая эпоха международной безопасности. Россия и мир: Монография / Отв. pед. О.П. Иванов. М.: Общество с ограниченной ответственностью «Проспект», 2023. С. 64–95.
  3. Шваб К. Четвертая промышленная революция. М.: Эксмо, 2016. 138 с.
  4. Acemoglu, D., Restrepo, P. (2019). Automation and new tasks: How technology displaces and reinstates labor. Journal of Economic Perspectives 33. No. 2. Pp. 3–30. DOI: 10.1257/jep.33.2.3
  5. Acemoglu, D. (2010). When Does Labor Scarcity Encourage Innovation? // Journal of Political Economy. Vol. 118, No. 6. Pp. 1037–1078.
  6. Brundage, M., et al. (2018). The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation. arXiv. arXiv:1802.07228. DOI: 10.48550/arXiv.1802.07228
  7. Brynjolfsson, E., Unger, G. (2023). The macroeconomics of artificial intelligence. Finance and development magazine. DECEMBER. Pp. 20–25. https://www.imf.org/en/Publications/fandd/issues/2023/12/Macroeconomics-of-artificial-intelligence-Brynjolfsson-Unger
  8. Carlini, N. et al. (2024). Poisoning Web-Scale Training Datasets is Practical, in 2024 IEEE Symposium on Security and Privacy (SP), San Francisco, CA, USA. Pp. 407–425. DOI: 10.1109/SP54263.2024.00179
  9. Collingridge, D. (1980). The social control of technology. London: Frances Pinter. 200 p.
  10. Frey, C.B. & Osborne, M.A. (2013). The Future of Employment: How Susceptible Are Jobs to Computerisation? Technological Forecasting and Social Change. T.114. DOI: 10.1016/j.techfore.2016.08.019
  11. Giannini, S. (2024). Generation AI: Navigating the opportunities and risks of artificial intelligence in education. UNESCO. 22 July. Available at: https://www.unesco.org/en/articles/generation-ai-navigating-opportunities-and-risks-artificial-intelligence-education (accessed: 02.03.2026).
  12. Hatzius, J., Briggs, J., Kodnani, D., Pierdomenico, G. (2023). The Potentially Large Effects of Artificial Intelligence on Economic Growth. Goldman Sachs Global Investment Research. 26 March. Available at: https://www.gspublishing.com/content/research/en/reports/2023/03/27/d64e052b-0f6e-45d7–967b-d7be35fabd16.html
  13. Hayami, Y., Ruttan, V.W. (1971). Induced Innovation and Agricultural Development, Staff Papers 13967, University of Minnesota, Department of Applied, DOI: 10.22004/ag.econ.13967
  14. Jonas, Hans (1984). The imperative of responsibility: in search of an ethics for the technological age. Chicago: University of Chicago Press. P. 202.
  15. Li, P., Yang, J., Islam, M.A., & Ren, S. (2023). Making AI Less "Thirsty": Uncovering and Addressing the Secret Water Footprint of AI Models. ArXiv, abs/2304.03271
  16. Loewen, P., Lee-Whiting, B., Arai, M., Bergeron, T., Galipeau, T., Gazendam, I., Needham, H., Slinger, L., Yusypovych, S. (2024). Global public opinion on artificial intelligence (GPO-AI). Schwartz Reisman Institute for Technology and Society. 109 p. Available at: https://srinstitute.utoronto.ca/public-opinion-ai
  17. Mandel, D.R. (2023). Artificial General Intelligence, Existential Risk, and Human Risk Perception. arXiv. https://arxiv.org/abs/2311.08698
  18. Matraeva, L. et al. (2020). The effects of digitalisation on the labour market: The case of Russia / L. Matraeva, E. Vasiutina, A. Belyak. Work Organisation, Labour and Globalisation. Vol. 14, No. 2. Pp. 31–45. DOI 10.13169/WORKORGALABOGLOB.14.2.0031. EDN IGOASI
  19. Morales, J. (2024). Research shows more than 80% of AI projects fail, wasting billions of dollars in capital and resources: Report. Tom’s Hardware. 28 Aug. Available at: https://www.tomshardware.com/tech-industry/artificial-intelligence/research-shows-more-than-80-of-ai-projects-fail-wasting-billions-of-dollars-in-capital-and-resources-repor (accessed: 24.03.2026).
  20. Motoki, F., Pinho Neto, V., Rodrigues, V. (2024). More human than human: measuring ChatGPT political bias. Public Choice. Vol. 198, Issue 1–2, January. Pp. 3–23.
  21. Occhipinti, J., & Haines, W. (2024). In the Shadow of Smith's Invisible Hand: Risks to economic stability and social wellbeing in the age of intelligence. P. 10, arXiv. https://arxiv.org/abs/2407.01545
  22. Perry, N., Srivastava, M., Kumar, D., and Boneh, D. (2023). Do Users Write More Insecure Code with AI Assistants? In Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security (CCS '23). Association for Computing Machinery, New York, NY, USA, 2785–2799. https://doi.org/10.1145/3576915.3623157
  23. Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and Policy Considerations for Deep Learning in NLP. ArXiv, abs/1906.02243
  24. Wachter, S., Mittelstadt, B., Floridi, L. (2017). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law. Vol. 7, No. 2. Pp. 76–99. DOI: 10.1093/idpl/ipx005
  25. Webb, L., & Schoenberger, D. (2024). Generative AI and the problem of existential risk. arXiv. https://arxiv.org/abs/2407.13365
  26. Whittlestone, J., & Clark, J. (2021). Why and How Governments Should Monitor AI Development. ArXiv, abs/2108.12427. https://arxiv.org/pdf/2108.12427.pdf
  27. Zhang, D., Finckenberg-Broman, P., Hoang, T. et al. (2024). Right to be forgotten in the Era of large language models: implications, challenges, and solutions. AI Ethics. https://doi.org/10.1007/s43681–024–00573–9
  28. Zhiqing, Bian (2024). Research on the Impact of Artificial Intelligence on the Labor Market. Highlights in Business, Economics and Management. Vol. 4. 24. Pp. 1036–1041.