Жасанды интеллекттің экономиканың құрылымдық өзгеруіне әсері

искусственный интеллект, цифровизация, структурная трансформация, диверсификация, ресурсозависимая экономика, экономический рост, рынок труда, занятость

1. Acemoglu, D., & Restrepo, P. (2018). The race between man and machine: Implications of technology for growth, factor shares, and employment. American Economic Review, 108(6), 1488–1542. https://doi.org/10.1257/aer.20160696

2. Acemoglu, D., & Restrepo, P. (2019). Automation and new tasks: How technology displaces and reinstates labor. Journal of Economic Perspectives, 33(2), 3–30. https://doi.org/10.1257/jep.33.2.3

3. Aghion, P., & Howitt, P. (1992). A model of growth through creative destruction. Econometrica, 60(2), 323– 351. https://doi.org/10.2307/2951599

4. Aitnazarov, T. M., Andabayeva, G. K., & Kakizhanova, T. I. (2024). The role of digital technologies and their impact on the economy of Kazakhstan. Central Asian Economic Review, 3, 86–99. https://doi.org/10.52821/2789-4401-2024-3-86-99 (In Russ.)

5. Bass, F. M. (1969). A new product growth model for consumer durables. Management Science, 15(5), 215– 227. https://doi.org/10.1287/mnsc.15.5.215

6. Bresnahan, T. F., & Trajtenberg, M. (1995). General purpose technologies: Engines of growth? Journal of Econometrics, 65(1), 83–108. https://doi.org/10.1016/0304-4076(94)01598-T

7. Brynjolfsson, E., Li, D., & Raymond, L. R. (2023). Generative AI at work (NBER Working Paper No. 31161). National Bureau of Economic Research. https://doi.org/10.3386/w31161

8. Bulkhairova, Zh. S., Alpeissova, Sh. E., & Kossymbayeva, Sh. I. (2023). [The current state and problems of economic digitalization in Kazakhstan]. Vestnik ENU im. L. N. Gumilyova. Ekonomicheskaya seriya = Bulletin of L.N. Gumilyov ENU. Economic Series, 1, 32–39. https://bulecon.enu.kz/index.php/main/article/view/110 (In Russ.)

9. Bureau of National Statistics. (2024). Bureau of National Statistics of the Republic of Kazakhstan. Retrieved December 15, 2025 from https://stat.gov.kz/en

10. Cockburn, I. M., Henderson, R., & Stern, S. (2018). The impact of artificial intelligence on innovation (NBER Working Paper No. 24449). National Bureau of Economic Research. https://doi.org/10.3386/w24449

11. Comin, D., & Hobijn, B. (2010). An exploration of technology diffusion. American Economic Review, 100(5), 2031–2059. https://doi.org/10.1257/aer.100.5.2031

12. Dell’Acqua, F., McFowland, E., Mollick, E. R., Lifshitz-Assaf, H., Kellogg, K., Rajendran, S., Krayer, L., Candelon, F., & Lakhani, K. R. (2023). Navigating the jagged technological frontier: Field experimental evidence of the effects of AI on knowledge worker productivity and quality (Harvard Business School Working Paper No. 24-013). Harvard Business School. https://doi.org/10.2139/ssrn.4573321

13. Denissova, O., Konurbayeva, Z., Kulisz, M., Yussubaliyeva, M., & Suieubayeva, S. (2025). Measuring the digital economy in Kazakhstan: From global indices to a contextual composite index (IDED). Economies, 13(8), 225. https://doi.org/10.3390/economies13080225

14. Eloundou, T., Manning, S., Mishkin, P., & Rock, D. (2023). GPTs are GPTs: An early look at the labor market impact potential of large language models. Science, 381(6654), 187–192. https://doi.org/10.1126/science.adh2586

15. Felten, E., Raj, M., & Seamans, R. (2021). Occupational, industry, and geographic exposure to artificial intelligence: A novel dataset and its potential uses. Strategic Management Journal, 42(12), 2195–2217. https://doi.org/10.1002/smj.3286

16. Felten, E., Raj, M., & Seamans, R. (2023). How will language modelers like ChatGPT affect occupations and industries? arXiv. https://doi.org/10.48550/arXiv.2303.01157

17. Goldfarb, A., & Tucker, C. (2019). Digital economics. Journal of Economic Literature, 57(1), 3–43. https://doi.org/10.1257/jel.20171452

18. Government of the Republic of Kazakhstan (2024). Concept for the development of artificial intelligence in the Republic of Kazakhstan for 2024–2029 [Kontseptsiya razvitiya iskusstvennogo intellekta v Respublike Kazakhstan na 2024–2029 gody] (Resolution No. 592, July 24, 2024). https://adilet.zan.kz/rus/docs/P2400000592 (In Russ.)

19. Hall, R. E., & Jones, C. I. (1999). Why do some countries produce so much more output per worker than others? Quarterly Journal of Economics, 114(1), 83–116. https://doi.org/10.1162/003355399555954

20. Hausmann R., & Klinger B. (2007). The structure of the product space and the evolution of comparative advantage. CID Working Paper No. 146. Harvard University. Retrieved December 15, 2025 from https://www.hks.harvard.edu/sites/default/files/centers/cid/files/publications/faculty-working-papers/146.pdf

21. Hausmann, R., Hidalgo, C. A., Bustos, S., Coscia, M., Simoes, A., & Yildirim, M. A. (2014). The atlas of economic complexity: Mapping paths to prosperity. MIT Press. https://doi.org/10.7551/mitpress/9647.001.0001

22. Hidalgo, C. A., & Hausmann, R. (2009). The building blocks of economic complexity. Proceedings of the National Academy of Sciences, 106(26), 10570–10575. https://doi.org/10.1073/pnas.0900943106

23. International Monetary Fund. (2024). Gen-AI: Artificial intelligence and the future of work (IMF Staff Discussion Note No. 2024/001). https://doi.org/10.5089/9798400262548.006

24. McKinsey Global Institute. (2023). The economic potential of generative AI: The next productivity frontier. McKinsey & Company. Retrieved December 15, 2025 from https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier

25. Mehlum, H., Moene, K., & Torvik, R. (2006). Institutions and the resource curse. Economic Journal, 116(508), 1–20. https://doi.org/10.1111/j.1468-0297.2006.01045.x

26. PricewaterhouseCoopers. (2017). Sizing the prize: What’s the real value of AI for your business and how can you capitalise? PwC. Retrieved December 15, 2025 from https://www.pwc.com.au/government/pwc-ai-analysis-sizing-the-prize-report.pdf

27. Romer, P. M. (1990). Endogenous technological change. Journal of Political Economy, 98(5), S71–S102. https://doi.org/10.1086/261725

28. Sachs, J. D., & Warner, A. M. (1995). Natural resource abundance and economic growth (NBER Working Paper No. 5398). National Bureau of Economic Research. https://doi.org/10.3386/w5398

29. Sachs, J. D., & Warner, A. M. (2001). The curse of natural resources. European Economic Review, 45(4–6), 827–838. https://doi.org/10.1016/S0014-2921(01)00125-8

30. Solow, R. M. (1956). A contribution to the theory of economic growth. Quarterly Journal of Economics, 70(1), 65–94. https://doi.org/10.2307/1884513

31. Solow, R. M. (1957). Technical change and the aggregate production function. Review of Economics and Statistics, 39(3), 312–320. https://doi.org/10.2307/1926047

32. Sultan, F., Farley, J. U., & Lehmann, D. R. (1990). A meta-analysis of applications of diffusion models. Journal of Marketing Research, 27(1), 70–77. https://doi.org/10.2307/3172552

33. Trajtenberg, M. (2018). AI as the next GPT: A political-economy perspective (NBER Working Paper No. 24245). National Bureau of Economic Research. https://doi.org/10.3386/w24245

34. Workforce Development Center. (2023). Assessment of the impact of automation, robotization and artificial intelligence on the labour market of the Republic of Kazakhstan [Otsenka vliyaniya avtomatizatsii, robotizatsii i iskusstvennogo intellekta na rynok truda Respubliki Kazakhstan]. https://erdo.enbek.kz/news/271 (In Russ.)