Still Waiting for the Shock: AI’s Limited Impact on Early-Career Vacancies, Skills and Tasks
Stefan Speckesser, Lei Xu
DOI: http://dx.doi.org/10.15439/2026F3431
Citation: Stefan Speckesser, Lei Xu (2026). Still Waiting for the Shock: AI’s Limited Impact on Early-Career Vacancies, Skills and Tasks. In M. Bolanowski, M. Ganzha, M. Grzegorowski, L. Maciaszek, M. Paprzycki, A. Paszkiewicz, D. Ślęzak (eds), Proceedings of the 21st Conference on Computer Science and Intelligence Systems (FedCSIS). ACSIS, Vol. 49, pages 101–109.
Abstract. This analysis of 620,000 UK apprenticeship vacancies suggests that AI is not the primary cause of declining opportunities, which instead stems from structural labor market weaknesses. The research utilized DistilRoBERTa to map digital skills from unstructured text and Google Gemini 1.5 Flash to dynamically generate an occupation-specific task taxonomy. Econometric results show no significant impact on overall vacancy volumes or technical task density. However, a modest increase in digital skill requirements was found for higher-level roles, suggesting AI complements advanced qualifications rather than displacing them. While intermediate roles have collapsed, findings attribute this to policy shifts like the Apprenticeship Levy rather than automation.
References
- Acemoglu, D. (2024). The Simple Macroeconomics of AI. Massachusetts Institute of Technology.
- Acemoglu, D. (2025). The Simple Macroeconomics of AI. Economic Policy, 40(121).
- Acemoglu, D., & Autor, D. (2011). Skills, Tasks and Technologies: Implications for Employment and Earnings. Handbook of Labor Economics, 4, 1043-1171.
- Acemoglu, D., & Restrepo, P. (2018). Artificial Intelligence, Automation, and Work. The Economics of Artificial Intelligence: An Agenda, 197-236. National Bureau of Economic Research.
- Acemoglu, D., & Restrepo, P. (2019). Automation and New Tasks: How Technology Displaces and Reinstates Labor. Journal of Economic Perspectives, 33(2), 3-30.
- Autor, D. (2024). Applying AI to Rebuild Middle Class Jobs. National Bureau of Economic Research, Working Paper 32140.
- Autor, D. H., Levy, F., & Murnane, R. J. (2003). The Skill Content of Recent Technological Change: An Empirical Exploration. The Quarterly Journal of Economics, 118(4), 1279-1333.
- Brynjolfsson, E., Li, D., & Raymond, L. R. (2023). Generative AI at Work. National Bureau of Economic Research, Working Paper 31161.
- Department for Education. (2026). Apprenticeships and traineeships: Academic year 2025/26. Explore Education Statistics. HM Government.
- Korinek, A. (2024). Economic policy challenges for the age of AI. National Bureau of Economic Research, Working Paper 32980.
- Office for National Statistics. (2026). Young people not in education, employment, and training (NEET), UK. HM Government.
- Reimers, N., & Gurevych, I. (2019). Sentence-BERT: Sentence embeddings using Siamese BERT-networks. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing.
- Sanh, V., Debut, L., Chaumond, J., & Wolf, T. (2019). DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv preprint https://arxiv.org/abs/1910.01108.
- The Sutton Trust. (2024). The apprenticeship levy and social mobility: Who benefits from the shift to higher-level training? The Sutton Trust.