Date
Publisher
arXiv
Generative AI (GenAI) is reshaping software engineering, raising concerns about how the development pathway through which juniors become seniors is being eroded. While macro statistics show a decline in junior hiring and controlled studies demonstrate the effects of AI on individual task performance, the mechanisms through which GenAI reshapes early-career development in real organizational and educational contexts have not been thoroughly examined. Through 14 semi-structured interviews with juniors at the threshold of entering software engineering and senior software engineers in South Korea, analyzed using Reflexive Thematic Analysis, we reveal a foundational pattern of Absorption -- GenAI redirects entry-level work into senior-AI workflows -- and three consequences: (1) juniors losing the productive struggle through which expertise once developed; (2) the structural reproduction of this loss through collective normalization of GenAI use in university classrooms; and (3) the perceptual asymmetry between seniors and juniors that prevents either side from correcting these dynamics on their own. By extending learning theory and situated cognition to organizational and institutional scales, we argue that GenAI appears to be absorbing not just specific categories of tasks but also parts of the pathway through which the next generation of seniors is formed. Preserving this pathway will require deliberate institutional design across classrooms, workplaces, and the evaluation criteria for juniors.
Study at a glance
- Question: How does generative AI reshape the early-career learning and developmental pathways of junior software engineers?
- Setting and data: South Korea, March 2026, 8 junior engineering students or recent graduates and 6 senior engineers across diverse software firms.
- Method: Semi-structured qualitative interviews analyzed using Reflexive Thematic Analysis (RTA).
Why it matters
If generative AI takes over entry-level programming tasks, junior engineers may lose the developmental scaffolding needed to become seniors, eroding the mechanisms through which professional expertise is reproduced.
Key findings
- Generative AI redirects routine, entry-level work directly into senior-AI workflows, eliminating the foundational tasks that historically gave junior developers learning opportunities.
- Juniors lose essential learning from productive struggle and failure, producing output without understanding, while competitive grading curves and collective peer use make AI adoption inescapable in university classrooms.
- Perceptual differences prevent self-correction, as seniors mistakenly assume juniors will independently gain hands-on experience, while juniors feel unable to develop unassisted competence.
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