The labor market and professions of the future: a comparative analysis of forecasts of large language models

Abstract

In the context of digitalization of the labor market, it is necessary to quickly forecast professions and take into account personnel shortages. The paper proposes an innovative approach to the analysis of vacancies, resumes and EdTech courses using large language models within the Perplexity.ai, DeepSeek.com, Qwen.ai services. The study is based on data from the trumvsem.ru website and a leading online learning platform. The methodology allows identifying the asymmetry of coverage and supply and predicting in-demand professional areas. The results demonstrate the effectiveness of language models in analyzing unstructured texts and identifying trends. Practical ingenuity lies in developing a tool for adapting programs and personnel policies. An approach to organizing data preparation and strategic planning for the development of human capital.