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September 25, 2026
AI Users Earn Up to 41.8% More Than Non-Users
Research conducted by economists at HSE University has revealed a significant correlation between the regular use of GenAI in the workplace and higher pay among Russian employees. The study found that individuals who frequently use GenAI in their professional activities earn notably more than those who reject these new tools or resort to them occasionally. The salary premium for highly qualified specialists reaches 41.8%. The article was published in the Voprosy Ekonomiki journal.
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Vincent Fardeau, Associate Professor at HSE ICEF, has reached a major career milestone: he recently published his paper ‘Asymmetric Thin Markets’ in the Journal of Financial Economics, successfully passed his major academic review, and received tenure. In this interview, Vincent discusses the story behind the paper, explains the concept of asymmetric thin markets, and shares his advice for young scholars aiming to publish in top-tier journals.
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A researcher at HSE University analysed data on 1,539 doctoral students from 161 Russian universities to identify which features of a thesis topic are associated with academic success and engagement. The most important factor was found to be personal interest in the research topic, which was associated with almost all key aspects of doctoral programme experience—from engaging with the academic supervisor to research activity and confidence about successfully defending the thesis. The findings have been published in Higher Education.

 

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Application of generative artificial intelligence for risk management of software projects

Proceedings of the Institute for System Programming of the RAS. 2024. Vol. 36. No. 2. P. 73–82.
Dzheiranian A. D., Plaksin M. A.

The article highlights an innovative approach to risk management in software projects using
generative artificial intelligence. It describes a methodology that involves the use of publicly available chatbots
to identify, analyze, and prioritize risks. The Crawford method is used as a basis for risk identification. The
authors propose specific formulations of requests to chatbots (instructs, prompts) that facilitate obtaining the
necessary information. The effectiveness of the methodology has been demonstrated on five small software
projects and a dozen of economic and organizational projects of significantly different scales, from small to
federal. This confirms its applicability and practical value.

Research target: Computer Science
Language: English
Full text
DOI
Text on another site
Keywords: управление рискамиrisk managementrisk identificationидентификация рисковchatbotgenerative artificial intelligenceгенеративный искусственный интеллектCrawford cardssoftware projectsчатботкарточки Кроуфордапрограммные проекты
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