How Artificial Intelligence Disrupts Engineering Progression
AI is disrupting career progression by eliminating the learning opportunities at each rung while simultaneously enabling people to perform above their experience level, Alasdair Allan explained in his talk Engineering Progression When AI Ate the Middle at QCon London. Fewer junior developers join the industry, and AI slows hiring at the entry level. By Ben Linders
Alasdair Allan's presentation at QCon London explores how artificial intelligence (AI) is reshaping career progression in engineering. Allan argues that AI eliminates learning opportunities at each stage of an engineer's career while simultaneously enabling professionals to perform beyond their current experience level. As a result, fewer junior developers are entering the industry, and the entry-level hiring process is being slowed down.
Traditionally, writing code was not the primary focus of an engineer's profession. However, with AI taking over code generation, the role of supervision becomes crucial, requiring coding skills. Allan questions where the next generation of engineers will come from if AI handles the work that traditionally trained engineers. The skills needed to validate AI-written code, such as understanding code patterns, system structures, and complexity, cannot be acquired in the same way as before.
Junior engineers are no longer building intuition about code quality through extensive reading and debugging experiences. Instead, they are relying on AI agents to summarize codebases and handle production incidents. The majority of engineering work is described as "blackfield," referring to legacy systems under high load and on the path to deprecation, with undocumented decisions that no one has time to address.
AI agents can analyze code and documentation but cannot comprehend production patterns or the nuances that experienced engineers possess.
The hiring of young workers has slowed in exposed occupations, meaning fewer junior developers are joining the industry. AI is not only transforming the work itself but also requiring supervision, which in turn slows down entry-level hiring. Organizations adopting AI coding tools are simultaneously weakening the pipeline that produces skilled supervisors for those tools.
The people who can effectively utilize AI are those who have acquired the necessary context through years of experience, and this pipeline is currently breaking, as argued by Allan.
Comparing programming with medical residency, Allan notes that the "scut work" in programming is essential for building judgment, even if it is not always efficient. Companies that continue to invest in structured learning paths and deliberate rotations through fundamental skills will have senior developers left in ten years, while others may retire by then.
Allan emphasizes the need to rebuild the career pipeline for engineers in the AI era, focusing on structured learning, measuring understanding rather than velocity, and treating context as infrastructure. He concludes by stressing that AI is a tool, not a teacher, and using it to bypass understanding is detrimental to one's future in the field.
Written by urgent.news from InfoQ's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
