Large Language Models and Symbolic Analysts: Re-Skilling and Job Displacement Among Journalists, Accountants, and Software Developers

Building upon Reich's (1991) classification of worker types and the relevance of Print/Digital Capitalism (Coulmas, 2022), this paper analyzes to what extent Large Language Models (LLMs) of generative AI imply re-skilling and job displacement among qualified workers. Our research questions focus on the extent to which language models, considered as symbol systems—including natural languages, numeric systems, and programming languages—are key in job performance, and how different types of workers are exposed to generative AI.

The automation of complex writing/speech, numerical calculations, and programming abilities challenges classical views on which groups are most exposed to technological change. The "linguistic part of work" (in our case 3 types of linguistic systems: natural language, numerical, software programming) is crucial in the knowledge economy; thus, machines capable of producing written text or "natural" speech are essential factors in understanding transformations in skilled labor.

Our methodology involves conducting in-depth interviews with 20 individuals from each of three professions that work with linguistic symbols: journalists, accountants, and software developers, totaling 60 interviews in Tarragona, Spain. The interviews capture experiences, perceptions, and strategies related to LLMs' impact on their professions. Data analysis focuses on AI tools, management changes, common themes and differences regarding re-skilling needs and job displacement risks.

By examining these professions, which represent three different types of symbolic/language systems, the study aims to deepen understanding of how generative AI affects symbolic analysts and to inform policy and educational strategies to mitigate adverse impacts while leveraging professional development opportunities. The findings are expected to provide insights into the evolving nature of work in the AI era and to challenge or support existing theories about technological displacement and the future of skilled labor.