AI is Transforming Engineer Career Development
Engineer Alasdair Allan pointed out the impact of AI on software engineers' career development in his keynote at QCon London 2025. AI is eliminating learning opportunities at each career stage while simultaneously enabling performance beyond what experience alone would provide. The number of junior engineers entering the industry is declining, which is also affecting entry-level hiring.
The widespread adoption of AI is fundamentally changing the structure of software engineers' career development. Engineer Alasdair Allan addressed this issue in his keynote "Engineering Progression When AI Ate the Middle" at QCon London, a technology conference held in London in 2025. He points out a pattern where AI is eliminating the learning opportunities that should naturally occur at each career stage while simultaneously enabling performance beyond what experience alone would provide.
It has long been considered essential for engineers to undergo "cumulative experience," starting with simple tasks and gradually transitioning to more difficult work. By completing foundational tasks such as code debugging, documentation maintenance, and test creation, engineers gain understanding of design principles and system architecture. However, with the emergence of AI tools, many of these "introductory tasks" are being automated, and the very opportunities for young professionals to gain experience are shrinking.
What Allan points out is the phenomenon of AI consuming "mid-level work." With AI tools, engineers with less experience can now perform advanced code generation and design assistance. Meanwhile, the number of junior engineers entering the industry is declining, and entry-level hiring is also being affected. In other words, AI is raising productivity while simultaneously removing the lower rungs of the career development ladder.
The fundamental question this change raises for the entire industry is: "Where do engineers gain experience?" In traditional career models, completing foundational work functioned as a process for building a technical foundation. However, if the work that forms this foundation is replaced by AI, the pathway from junior to senior roles becomes unclear. How engineering organizations redesign their talent development systems is increasingly becoming the focal point.
On the other hand, the productivity gains from AI are real. The ability of less experienced engineers to handle complex tasks has the positive aspect of broadening individual career opportunities. However, Allan's core argument is that this "quick deployment to productive roles" comes at the cost of losing opportunities for gradual, staged growth. The question of where to strike a balance between short-term productivity and long-term talent development is now being posed as a strategic business decision.
The decline in entry-level hiring and the decrease in junior engineers entering the industry are already observable trends. If this trend continues, it could potentially affect the supply of senior engineers and technical leaders in the medium to long term. The question of "what is a career in an era where AI has become a standard tool" is increasingly becoming a topic that demands answers not just from individual engineers, but also from companies and educational institutions.
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