Generative AI Is Cutting Off the Path That Turns Junior Coders into Senior Engineers

Generative artificial intelligence has been celebrated as a productivity engine for the software industry, rapidly handling the coding, debugging, and documentation tasks that once filled the working days of entry-level programmers. But a new study from Seoul National University argues that the industry may be quietly dismantling the very apprenticeship system that has always produced its most valuable experts. In research accepted to the 2026 AAAI/ACM Conference on AI, Ethics, and Society (AIES 2026), a team led by Professor Taesup Moon of the Department of Electrical and Computer Engineering finds that what generative AI is eroding is not merely junior developers’ employment prospects, but the developmental pathway through which novices historically grew into senior professionals. The paper, titled “Who Will Become the Next Senior? How Generative AI Erodes the Development Pathway in Software Engineering,” raises a question that has received surprisingly little attention amid the rush toward automation: if AI absorbs the work of junior developers, where will the senior developers of the future come from?

The research team approached the problem not by trying to pin down the causes of declining recruitment, but by examining how the roles of junior developers and the process of building professional expertise are actually changing on the ground. They chose South Korea as a case study in which these shifts appear in an early and highly visible form. According to data cited in the paper, AI is involved in 51.8 percent of work-related activities in Korea, nearly twice the comparable figure for the United States, while conventional entry-level hiring has contracted sharply. Entry-level job postings at the fifteen largest U.S. technology companies fell by 25 percent between 2023 and 2024, and IT job postings in Korea declined by 43 percent over the same period, with positions targeting entry-level candidates accounting for just 4.4 percent of all postings. The researchers caution that these declines cannot be attributed solely to generative AI, since post-pandemic workforce adjustments and the economic slowdown have also played a role, but the structural transformation of junior work was unmistakable in their data.

At the heart of the study are semi-structured interviews, each lasting approximately fifty minutes, with fourteen participants: six senior developers currently working in the industry and eight junior participants preparing to enter software development careers. The senior participants were required to have at least six years of professional experience and to have entered the workforce before the emergence of generative AI, while the junior participants were undergraduates whose university years spanned both the pre-AI and post-AI eras. The team analyzed the transcripts using reflexive thematic analysis, a qualitative method in which researchers iteratively examine their own interpretive role while identifying recurring patterns across interviews. Four major themes emerged, and together they sketch a system in which the loss of junior-level work is inseparable from the loss of junior-level learning.

The first theme was the shift of junior-level tasks toward what the researchers describe as “senior plus AI” workflows. Senior developers increasingly use AI to directly handle basic implementation, debugging, and documentation that would previously have been assigned to junior staff, and in doing so they absorb the very tasks through which novices once gained hands-on experience. One startup founder interviewed for the study said the company had substantially reduced its junior workforce without any measurable impact on productivity, asking pointedly, “What makes a junior developer better than a KRW 100,000-per-month AI subscription?” The economic logic of that question is difficult to dispute in the short term, and it explains why individual firms have strong incentives to make the substitution. The study’s point is that the costs of this rational choice do not disappear; they are displaced onto a different group of people at a different point in time.

That displacement is captured in the second theme: what is disappearing is not simply work, but the “opportunity to fail.” The researchers frame this through the concept of “productive struggle,” the well-documented principle in learning science that when learners make mistakes and correct them on their own, they develop not only knowledge but also the metacognitive ability to recognize what they do not know and to detect errors in the results they produce. Junior participants reported that they could now achieve good results with far less effort than before, yet many described a troubling sense of “not knowing what I don’t know.” One participant who took two courses in the same field received the same grade in both, but said that one course left them with actual knowledge while the other left them only with “the ability to use AI.” The single difference between the courses was whether the assignments could be completed using AI. The researchers found that current educational assessment methods do not adequately capture this distinction, meaning grades can mask profound differences in what students have actually learned.

Senior developers voiced the same concern from the other side of the career ladder. One participant explained, “The reason you become a senior is that experience teaches you what not to do. Now juniors can no longer learn from bad examples.” This observation connects directly to the study’s theoretical grounding in “situated cognition,” the idea that the problems a person can perceive depend on the position they occupy. Senior developers and the organizations with the power to address the problem may find it difficult to fully appreciate the circumstances facing juniors, while juniors who directly experience the problem lack the power to change educational and hiring structures. The asymmetry is captured starkly in the words of another senior participant: “We have 20 years of accumulated experience, so we can judge whether AI outputs are right or wrong. The next generation will not be able to reach that position. So we are fine.”

The third and fourth themes underscore that these dynamics are structural rather than matters of individual choice, and that they will not correct themselves. When every one of a student’s peers uses generative AI, grading on a curve effectively becomes a mechanism that compels AI use, since opting out means accepting a competitive disadvantage. In the courses taken by the interview participants, AI use was rarely restricted, so the loss of failure-based learning was built into university classrooms just as surely as into workplaces. Meanwhile, because seniors and juniors perceive the same situation so differently, no natural feedback loop exists to trigger correction. The researchers also found that the problem did not originate with generative AI alone: senior developers themselves largely built their expertise through hands-on work and trial and error rather than through formal training systems. In this sense, generative AI has not eliminated a newly created development pathway; it has accelerated the erosion of a pathway that was never institutionally protected in the first place.

The study argues that the solution lies in deliberate institutional protection rather than individual effort, and it draws on examples from other high-risk fields where automation has already forced this reckoning. In aviation, the Federal Aviation Administration’s SAFO 13002 advises pilots to maintain opportunities for manual flying so that automation does not erode their manual flying skills, and the nuclear industry requires regular simulator retraining to keep operators’ response capabilities sharp. These are examples of industries deliberately preserving the experiences necessary for developing and maintaining expertise even when machines can perform the underlying tasks. The research team proposes analogous reforms across three areas of software engineering. At universities, courses in which AI cannot achieve the learning objectives on behalf of students should be designated as required, and “reducing AI dependence” should serve as a criterion for evaluating educational quality. In hiring, employers should evaluate not only candidates’ ability to use AI to produce results quickly, but also their ability to recognize gaps in their own knowledge and detect errors while working alongside AI. Within companies, learning opportunities should be deliberately created for entry-level developers, for example by assigning them small modifications to actual products and letting them experience the full process from commits and code review through deployment.

Professor Moon, who supervised the research, framed the findings as a warning about a hidden ledger. “Some of the productivity we are gaining today may effectively be borrowed in advance from the expertise of the next generation,” he said. “Because the cost is borne by a different group and at a different point in time from those making the decisions, it is structurally difficult to recognize. This study is significant because it empirically reveals that blind spot.” He added that, as a department that develops AI technologies itself, SNU bears a particular responsibility to ask what those technologies are doing to the development of the next generation, and that the team plans follow-up research focused on university education and on incorporating the findings into the design of the undergraduate curriculum. The paper’s first author, Ph.D. student Sumin Yu of SNU’s M.IN.D Lab, conducts empirical research on how generative AI affects human learning, decision-making, and social institutions, and plans to continue studying the interaction between AI technologies and society across a range of fields.

The significance of the work extends beyond software engineering. AIES, jointly organized by the Association for the Advancement of Artificial Intelligence and the Association for Computing Machinery, is regarded alongside ACM FAccT as one of the two leading venues for research on the ethical and societal impacts of AI, and the ninth edition of the conference will be held in Malmö, Sweden, from October 12 to 14. The research was supported by the National Research Foundation of Korea, the Institute of Information & Communications Technology Planning & Evaluation, and the BK21 FOUR Education and Research Program at Seoul National University. What the study ultimately documents is a paradox at the center of the AI productivity boom: the judgment that makes senior engineers valuable is produced by exactly the kind of slow, error-prone, hands-on work that AI now performs so cheaply. An industry that optimizes away that work may find, a decade from now, that it has optimized away its own future experts, and the bill for today’s efficiency gains will come due in the form of a generation that never learned what it does not know.

Subject of Research: How generative AI erodes the professional development pathway of junior software developers

Article Title: SNU professor Taesup Moon’s team reveals how generative AI is eroding the development pathway for software developers

Article References: SNU professor Taesup Moon’s team reveals how generative AI is eroding the development pathway for software developers. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: generative AI, software engineering, junior developers, skill development, productive struggle, situated cognition, AIES 2026, Seoul National University, entry-level hiring, AI ethics, developer education, automation