Tech Leaderism

Junior Devs and AI

We rightfully celebrate AI for removing the "grunt work" from software development. It is liberating to see boilerplate code, unit test scaffolding, and simple refactors handled instantly. However, as we embrace this efficiency, we must also be mindful of the subtle role that repetitive work plays in professional development.

For decades, the path to seniority was paved with these smaller, lower-risk tasks. Debugging a simple race condition or writing a basic CRUD API was not just about shipping features; it was the training ground where intuition was built. It was through these struggles that engineers learned not just how code works, but how it breaks. If we automate the "learning curve" entirely, we risk removing the very ladder that junior engineers climb to reach expertise.

There is a distinct difference between generating a solution and understanding the trade-offs behind it. If a junior engineer’s primary role shifts too quickly from writing code to reviewing AI output, they may develop a surface-level familiarity with syntax without the deep, structural understanding that defines a senior architect.

This does not mean we should reject these tools. Instead, it places a new responsibility on engineering leadership. We can no longer rely on the work itself to teach our teams. Mentorship must become more intentional, focusing on the "why" rather than just the "how." We need to ensure that as our tools get smarter, our training processes evolve to ensure the next generation of engineers is still challenged to think deeply, not just prompt efficiently.