AI Won’t Kill Juniors. It Will Expose Seniors.

Everyone fears for the juniors. But the engineers who stopped growing at the wrong layer have more to lose.

The tech industry has a new consensus: AI will kill junior engineering jobs. Look at any discussion thread, and you’ll find the same narrative. Juniors are doomed. They’ll never learn to code properly. The entry-level pipeline is broken.

I’m not so sure. When I look at junior engineers today, I see people who are used to learning. They came up through boot camps, YouTube tutorials,and constantly shifting frameworks. Adapting is what they do. They might struggle for a year or two, but they’ll figure it out.

The engineers I’m worried about are the senior ones.

Sure, not all of them. But the ones who plateaued at “code craftsman” and never moved up.

I’ve seen it play out already. A standup where someone proudly reports they spent the day fixing a batch of bugs and shipping a couple of pull requests. The rest of the team glances at each other. They’re thinking: *that’s ten minutes of Claude Code. Why did you spend eight hours in your IDE?*

This isn’t new. We’ve seen it before. When bash gave way to Perl. When Java replaced C for most applications. Every paradigm shift leaves some people behind. Maybe 10%, maybe 20%, clinging to the old way because it’s what they know.

But AI is different. The shift is faster. The impact is more massive. And the reach is exponential.

Here’s the pattern I see. When I started programming, you’d learn assembly. Then you’d switch to C because life is short. Then Python, because life is really short. Each jump felt like cheating to the previous generation, and each one freed you to think at a higher level.

AI is the next rung on that ladder. I hope schools are teaching this now: learn to write code by hand first (you need to understand what you’re abstracting), then switch to AI-assisted development. Just like you learned assembly to understand memory, then moved on. Though knowing how slow institutions adapt, I’m not holding my breath.

The engineers who get this are thriving. Staff engineers, principal engineers, people whose job was already 70% architecture, cross-team coordination, and system design. They only coded 30% of the time anyway. Now they use AI to multiply that 30% and have even more impact. For them, AI is a force multiplier on an already leveraged role.

But there’s another group. Senior engineers, five to ten years in, who still think their job is writing code 90% of the time. They never thought deeply about data models. Never cared much about architecture. Never moved toward the work that would make them staff or principal.

Their entire value was “writing proper, clean code that runs well and passes the linter.”

That value just evaporated.

And here’s what makes it worse: working with AI is fundamentally communication work. The engineers who thrive are the ones who already know how to share context, explain problems to colleagues, and filter signal from noise across teams.

I’ve watched engineers struggle with AI because they won’t invest in communication. They type “fix this bug” without the stack trace, without the constraints, without explaining how production differs from their local setup. They keep the context in their head because explaining feels costly. The result is garbage, and they blame the tool.

What they don’t see: AI compounds. The more context you feed it about your project, the better it gets. But that requires upfront investment in articulation. If you spent your career avoiding that investment with humans, you’ll prevent it with AI too.

I don’t have a clean solution. The engineers who won’t adapt will stagnate. They might find work in industries that are slow to change. But it won’t be a great career. It never is when you’re holding onto the last paradigm.

The engineers at risk aren’t the ones who don’t know enough yet. They’re the ones who stopped growing at the wrong layer. Juniors will climb. The question is whether the seniors stuck in the middle will climb with them.