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QUANTIFYING THE ROLE OF LARGE LANGUAGE MODELS IN REBUILDING UKRAINE: LABOR AUTOMATION AND COST SAVINGS

Роман Семко · Helvetica Publishing House · 2026

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This study examines the potential impact of Large Language Models (LLMs) on the Ukrainian labor market. For the first time, it attempts to estimate the share of employees that can be replaced by generative AI technology during the next five years. In addition, total cost savings were calculated based on forecasted wage and LLMs diffusion. Assuming no labor market frictions, the results indicate a modest but still significant potential for automation starting from 0.2% in 2026 based on pure LLM technology and reaching up to 2.6% of the labor force by 2030 with additional LLM-augmented software. Finally, net cost savings in 2026 are projected at approximately 7.9 billion UAH annually increasing to a maximum of 122.0 billion UAH by 2030. The results are relevant for government authorities and businesses supporting decision-making in human resource management, automation strategies, and the development of upskilling and reskilling programs.

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APA 7

Семко, Р. (2026). QUANTIFYING THE ROLE OF LARGE LANGUAGE MODELS IN REBUILDING UKRAINE: LABOR AUTOMATION AND COST SAVINGS. https://doi.org/10.32782/2524-0072/2026-85-78

MLA

Семко, Роман. "QUANTIFYING THE ROLE OF LARGE LANGUAGE MODELS IN REBUILDING UKRAINE: LABOR AUTOMATION AND COST SAVINGS." 2026. https://doi.org/10.32782/2524-0072/2026-85-78.

Chicago

Семко, Роман. 2026. "QUANTIFYING THE ROLE OF LARGE LANGUAGE MODELS IN REBUILDING UKRAINE: LABOR AUTOMATION AND COST SAVINGS.". https://doi.org/10.32782/2524-0072/2026-85-78.

Harvard

Семко, Р. 2026, QUANTIFYING THE ROLE OF LARGE LANGUAGE MODELS IN REBUILDING UKRAINE: LABOR AUTOMATION AND COST SAVINGS, Helvetica Publishing House, available at: https://doi.org/10.32782/2524-0072/2026-85-78 [Accessed 6 Aug. 2026].

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Titolo
QUANTIFYING THE ROLE OF LARGE LANGUAGE MODELS IN REBUILDING UKRAINE: LABOR AUTOMATION AND COST SAVINGS
Autore / collaboratori
Роман Семко
Editore
Helvetica Publishing House
Anno di pubblicazione
2026
ISSN
2524-0072
ISSN
2524-0072
Lingua
Inglés

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