Sep 2026· Journal of economics, finance and management studies· 0 citations
Abstract
This paper investigates if companies in India's information technology (IT) services sector and global capability centres that have adopted artificial intelligence (AI) grow at a faster pace when compared to those that have not adopted such technologies and explores the implications of this transformation for labour demand.The analysis uses the data collected through the financial reports of firms, labor market information, and public reports and It adopts graphical and comparative analysis to study the dynamics of AI adoption, revenue expansion, job creation, and recruitment. The results reveal that companies characterized by a greater degree of AI adoption have much better revenue indicators than those characterized by a lower degree of AI adoption, thus confirming the assumption that AI makes companies more productive and efficient and improves their competitive position. At the industry level, however, there is a situation where revenue expansion is much faster than job creation, which indicates weakening relationship between output growth and labor demand. Though net hiring slows down, total job roles in the industry still grow which suggests that AI does not eliminate jobs, but restructures labor demand since the trend now indicates that routine tasks are becoming less and less common. The study concludes that AI is shifting India’s technology sector from labour-cost driven growth to innovation and productivity driven expansion, making reskilling and workforce adaptability essential for inclusive and sustainable growth.
GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations...
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This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.
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Empirically, PRISM reduces the end-to-end time for data selection and model tuning to just 30% of conventional pipelines, and achieves this efficiency while simultaneously enhancing performance, surpassing models fine-tuned on the full dataset across eight multimodal and three language understanding benchmarks.
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With $2.1 million funding from Google.org, the open-source Public Transit Intelligence Hub will unify public transit monitoring, operations, and passenger communication.
Professor Sherry Turkle’s new book, “Artificial Intimacy,” offers a withering critique of chatbots and the antisocial dynamics she believes they encourage.
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