1. Кузьминов Я., Кручинская Е. (2024) Потенциал генеративного искусственного интеллекта для решения профессиональных задач. Форсайт, 18(4), 67-76. DOI: 10.17323/2500-2597.2024.4.67.76 EDN: CZHXZV
2. Acemoglu D. (2025) The simple macroeconomics of AI. Econоmic Policy, 40(121), 13-58. DOI: 10.1093/epolic/eiae042 EDN: GSVCTG
3. Aleskerov F., Petrushchenko V. (2016) DEA by sequential exclusion of alternatives in heterogeneous samples. International Journal of Information Technology & Decision Making, 15(1), 5-22. DOI: 10.1142/S021962201550042X EDN: WQMPVD
4. Bang Y., Cahyawijaya S., Lee N., Dai W., Su D., Wilie B., Lovenia H., Ji Z., Yu T., Chung W., Do Q.V., Xu Y., Fung P. (2023) A multitask, multilingual, multimodal evaluation of chatgpt on reasoning, hallucination, and interactivity (arXiv preprint 2302.04023). DOI: 10.18653/v1/2023.ijcnlp-main.45
5. Bender E.M., Gebru T., McMillan-Major A. Shmitchell S. (2021) “On the dangers of stochastic parrots: can language models be too big? In: FAccT 2021 - Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, New York: Association for Computing Machinery, pp. 610-623. DOI: 10.1145/3442188.3445922
6. Biderman S., Schoelkopf H., Sutawika L., Gao L., Tow J., Abbasi B., Aji A.F., Ammanamanchi P.S., Black S., Clive J., DiPofi A., Etxaniz J., Fattori B., Forde J.Z., Foster Ch., Jaiswal M., Lee W.Y., Li H., Lovering Ch., Muennighoff N., Pavlick E., Phang J., Skowron A., Tan S., Tang X., Wang K.A., Winata G.I., Yvon F., Zou A. (2024) Lessons from the trenches on reproducible evaluation of language models (arXiv Preprint 2405.14782). DOI: 10.48550/arXiv.2405.14782
7. Cai Z., Wang Y., Sun Q., Wang R., Gu C., Yin W., Lin Z., Yang Z., Wei C., Shi X., Deng K., Han X., Chen Z., Li J., Fan X., Deng H., Lu L., Li B., Liu Z., Wang Q., Lin D., Yang L. (2025) Has GPT-5 Achieved Spatial Intelligence? An Empirical Study (arXiv Preprint 2508.13142v1). DOI: 10.48550/arXiv.2508.13142
8. Challapally A., Pease C., Raskar R., Chari P. (2025) The GenAI Divide: State of AI Iin Business 2025, Cambridge, MA: MIT. Chang Y., Wang X., Wang J., Wu Y., Yang L., Zhu K., Chen Н., Yi X., Wang C., Wang Y., Ye W., Zhang Y., Chang Y., Yu P.S., Yang Q., Xie X. (2024) A survey on evaluation of large language models. ACM Transactions on Intelligent Systems and Technology, 15(3), 1-45. DOI: 10.48550/arXiv.2307.03109
9. CompTIA (2024) State of the Tech Workforce, Downers Grove, IL: The Computing Technology Industry Association (CompTIA).
10. Deng Y., Xia C. S., Peng H., Yang C., Zhang L. (2023) Large language models are zero-shot Fuzzers: fuzzing deep-learning libraries via large language models. In: ISSTA 2023 - Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis, New York: Association for Computing Machinery, pp. 423-435. DOI: 10.1145/3597926.3598067
11. Fu Y., Weng Z. (2024) Navigating the ethical terrain of AI in education: A systematic review on framing responsible human-centered AI practices. Computers and Education Artificial Intelligence, 7(1), 100306. DOI: 10.1016/j.caeai.2024.100306 EDN: XMSTLI
12. Georgiou G.P. (2025) Capabilities of GPT-5 across critical domains: Is it the next breakthrough? (arXiv preprint 2508.19259). DOI: 10.48550/arXiv.2508.1925
13. Gładysz B., Despotis D., Kuchta D. (2024) Application of data envelopment analysis to IT project evaluation, with special emphasis on the choice of inputs and outputs in the context of the organization in question. Journal of Information and Telecommunication, 8(3), 301-314. DOI: 10.1080/24751839.2023.2286764
14. Gur I., Furuta H., Huang A. V., Safdari M., Matsuo Y., Eck D., Faust A. (2024) A real-world WebAgent with planning, long context understanding, and program synthesis (arXiv preprint 2307.12856). DOI: 10.48550/arXiv.2307.12856
15. Hajikhani A., Cole C. (2024) A critical review of large language models: Sensitivity, bias, and the path toward specialized AI. Quantitative Science Studies, 5(3), 736-756. DOI: 10.1162/qss_a_00310
16. Hu X., Xu Z., Ling Z., Jin Z., Du S. (2024) Emerging Synergies Between Large Language Models and Machine Learning in Ecommerce Recommendations (arXiv preprint 2403.02760). DOI: 10.48550/arXiv.2403.02760
17. Huang Y., Tang K., Chen M.A. (2024) Comprehensive Survey on Evaluating Large Language Model Applications in the Medical Industry (arXiv preprint 2404.15777). DOI: 10.48550/arXiv.2404.15777
18. Kouzminov Y., Kruchinskaia E. (2024) The Evaluation of GenAI Capabilities to Implement Professional Tasks. Foresight and STI Governance, 18(4), 67-76. DOI: 10.17323/2500-2597.2024.4.67.76 EDN: CZHXZV
19. Laskar M.T.R., Fu X.-Y., Chen C., Bhushan T.N.S. (2023) Building real-world meeting summarization systems using large language models: A practical perspective. In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track, Singapore: Association for Computational Linguistics, pр. 343-352. DOI: 10.18653/v1/2023.emnlp-industry.33
20. Lee J., Stevens N., Han S.C., Song M. (2024) A survey of large language models in finance (finllms) (arXiv preprint 2402.02315). DOI: 10.1007/s00521-024-10495-6
21. Li Y., Wang S., Ding H., Chen H. (2023) Large language models in finance: A survey. In: Proceedings of the 4th ACM International Conference on AI in Finance, New York: Association for Computing Machinery, рр. 374-382. DOI: 10.1145/3604237.3626869
22. Liu J., Gong Y., Zhu J., Titah R. (2021) Information technology and performance: Integrating data envelopment analysis and configurational approach. Journal of the Operational Research Society, 73(6), 1278-1293. DOI: 10.1080/01605682.2021.1907238 EDN: AYKITF
23. Mayer H., Yee L., Chui M., Roberts R. (2025a) Superagency in the Workplace. Empowering People to Unlock AI’s Full Potential, New York: McKinsey & Company. Noffsinger J., Patel M., Sachdeva P. (2025c) The Cost of Compute: A $7 Trillion Race to Scale Data Centers, New York: McKinsey & Company.
24. Raza M., Jahangir Z., Riaz M.B., Saeed M.J., Sattar M.A. (2025) Industrial applications of largelanguage models., 15, 13755. DOI: 10.1038/s41598-025-98483-1
25. Ren Q., Jiang Z., Cao J., Li C., Liu Y., Huo S., He T., Chen Y. (2024) A survey on fairness of large language models in e-commerce: progress, application, and challenge (arXiv preprint 2405.13025). DOI: 10.48550/arXiv.2405.13025
26. Saleh Y., Abu Talib M., Nasir Q., Dakalbab F. (2025) Evaluating large language models: a systematic review of efficiency, applications, and future directions. Frontiers in Computer Science, 7, 1523699. DOI: 10.3389/fcomp.2025.1523699 EDN: XLGCCE
27. Shi J., Mei J., Zhu L., Wang Y. (2024) Estimating the Innovation Efficiency of the Artificial Intelligence Industry in China Based on the Three-Stage DEA Model. IEEE Transactions on Engineering Management, 71, 9217-9228. DOI: 10.1109/TEM.2023.3323292
28. Stanford University (2025) Artificial Intelligence Index Report 2025, Stanford, CA: The Stanford Institute for Human-Centered Artificial Intelligence.
29. Trott S., Jones C., Chang T., Michaelov J., Bergen B. (2023) Do large language models know what humans know? Cognitive Science, 47, 13309. DOI: 10.1111/COGS.13309 EDN: EMGVSQ
30. Wang S., Xu Т., Li Н., Zhang С., Liang J., Tang J., Yu P.S., Wen Q. (2024) Large language models for education: A survey and outlook (arXiv preprint 2403.18105). DOI: 10.48550/arxiv.2403.18105
31. Wen H., Li Y., Liu G., Zhao S., Yu Т., Li T. J.-J., Jiang S., Liu Y., Zhang Y., Liu Y. (2024) AutoDroid: LLM-powered Task Automation in Android. Paper presented at the ACM MobiCom’24 International Conference on Mobile Computing and Networking, September 30 - October 4, 2024. 10.1145/3636534.3649379 WEF (2025) The Future of Jobs Report 2025, Geneva: World Economic Forum. DOI: 10.1145/3636534.3649379WEF(2025)TheFutureofJobsReport2025
32. Xu H., Gan W., Qi Z., Wu J., Yu P.S. (2024) Large Language Models for Education: A Survey (arXiv preprint 2405.13001). DOI: 10.48550/arXiv.2405.13001
33. Yee L., Chui M., Roberts R., Smit S. (2025) Technology Trends Outlook 2025, New York: McKinsey & Company.
34. Zhao H., Liu Z., Wu Z., Li Y., Yang T., Shu P., Xu S., Dai H., Zhao L., Jiang H., Pan Y., Chen J., Zhou Y., Mai G., Liu N., Liu T. (2024) Revolutionizing finance with LLMs: An overview of applications and insights (arXiv preprint 2401.11641). DOI: 10.48550/arXiv.2401.11641
35. Zhao W.X., Zhou K., Li J., Tang T., Wang X., Hou Y., Min Y., Zhang B., Zhang J., Dong Z., Du Y., Yang C., Chen Y., Chen Z., Jiang J., Ren R., Li Y., Tang X., Liu Z., Liu P., Nie J.Y., Wen J.R. (2023) A Survey of Large Language Models (arXiv preprint 2303.18223). DOI: 10.48550/arXiv.2303.18223