AI Is Taking on Entry-Level Engineering Work. Who Trains the Young Engineers?

In recent times, artificial intelligence has been increasingly involved in handling entry-level engineering tasks. This shift has sparked concerns about how the next generation of engineers will acquire the necessary skills to tackle complex issues independently. AI tools are now prevalent in software development, with a significant portion of developers utilizing them daily.

The State of Tech Talent report indicates a substantial decline in entry-level hiring at major technology companies and startups since 2019. While AI boosts productivity, it simultaneously reduces the traditional entry point for aspiring engineers, making the training obtained from routine work increasingly crucial. Many valuable lessons in the author's early career were acquired through repetitive tasks that looked mundane from the outside.

Handling access administration, investigating permission failures, and troubleshooting recurring issues in different scenarios helped the author develop a structured approach to fault diagnosis. The author emphasizes the importance of learning from these experiences, as they foster the ability to diagnose unfamiliar problems systematically.

When coaching colleagues, the author observes a stark contrast in how engineers approach errors. Some rely on AI for immediate solutions, while others meticulously rule out possibilities and build explanations based on evidence. The author highlights that AI can expedite the initial diagnosis but requires a solid understanding of the system to recover when solutions are incorrect.

Businesses should be cautious about removing repetitive tasks from junior engineers' workloads entirely. Automation can still provide opportunities for junior engineers to learn how to investigate failures from start to finish. Reviewing AI-generated fixes, explaining the rationale behind recommendations, and testing alternative causes are essential skills that should not be overlooked.

Even when AI handles the first pass, junior engineers should still be given the chance to progress through the incident resolution process. Research shows that 90% of technology professionals now use AI in their work, and over 80% believe it has increased their productivity. However, the time saved through automation often gets reallocated to auditing and verification, which are integral parts of the apprenticeship process.

In the future, the workforce might consist of highly efficient AI users but lack the independent technical judgement gained from hands-on experience. Companies should consider the implications of removing these learning opportunities from junior roles, as it could lead to a workforce that is proficient with AI systems but less capable of handling unique production problems.

The author argues that the true value of junior engineers lies in their ability to navigate unfamiliar technical problems, even when those problems prove frustrating. To thrive in an AI-driven environment, engineers must master the tools while retaining the ability to discern when AI solutions fall short. Therefore, it is crucial for companies to ensure that junior engineers continue to receive sufficient real-world problems to diagnose and solve, fostering the development of critical independent judgement.