Same Job, Faster Team
Why I'm not threatened by AI as an Engineering Manager
Picture it: tomorrow morning, five new developers join your team. They’re sharp, they’re eager, and they’re almost ready to contribute. Not quite senior engineers (they’ll need direction, they’ll need their work reviewed, and they won’t always get context right the first time). But they show up every day, they never complain about the repetitive stuff, and they can crank through the work nobody else wants to touch.
That’s roughly what AI looks like for an engineering team right now.
I’ve told my team: give AI the jobs you don’t want to do yourself. The mundane. The repetitive. The first draft you’d rather not stare at a blank screen for. Not because that work doesn’t matter, but because your judgment is better spent elsewhere, and these new “teammates” are genuinely good at it.
But here’s where it gets interesting for EMs.
The role has already changed once. Remote-first work took away proximity as a leadership crutch. No more reading the room, no more solving problems by walking over to someone’s desk. That forced a better kind of leadership: more intentional, more explicit, built on trust and clarity rather than visibility. Some of us grew through that transition, even if it wasn’t easy.
AI is the next shift. And in some ways, it’s a similar lesson: you can’t manage what you don’t understand, and you can’t lead a team that’s using tools you’ve never thought seriously about.
The core of the Engineering Manager job hasn’t changed in this AI-first world. Building strong teams, removing blockers, providing direction, shaping a roadmap that actually matters; those are still fundamentally human problems, and they matter more now, not less. What’s changing is the scale at which your team can operate, and the cost of getting things wrong.
When execution gets cheaper and faster, the expensive mistakes shift upstream. Ambiguity becomes expensive. Misalignment becomes expensive. Poor prioritization becomes really expensive. Now your team can sprint hard in the wrong direction before anyone notices, and at times are often encouraged to do so by people yelling the “AI Victory Chant”. The Engineering Manager’s job isn’t just to unblock people anymore. It’s to make sure the direction is right before they hit the accelerator.
So what does thoughtful look like in practice?
To me, it starts with normalizing AI as part of the workflow: not a novelty, not a shortcut people feel vaguely guilty about, but a legitimate part of how the team operates. It means helping your engineers figure out not just how to use it, but when and where it adds leverage, and where human judgment is the thing that actually matters. And it means holding the quality bar steady even as output speeds up, because speed without judgment is just a faster way to build the wrong thing. This has been the job of line managers in technology since day one; AI is just multiplying the speed at which we build.
The human side of the job doesn’t shrink. If anything, it becomes the foundation everything else depends on. People still need mentorship, context, someone thinking about their growth and their place on the team. That work is irreplaceable, and it’s also what separates a team that uses AI well from one that just uses it a lot.
Here’s what I’d say to any Engineering Manager sitting on the fence about this: start small, but start quickly. The goal isn’t to hand your team over to the AI overlords; it’s to hand AI the work that was quietly grinding your team down. The repetitive tickets, the boilerplate, the first drafts nobody wanted to write. The long list of bug triage that no one was excited about doing. Free your engineers up for the work that actually needs them. The creative problem solving that engineers enjoy. The collaboration that matters.
AI doesn’t replace a strong engineer, it force-multiples them. Your job as an Engineering Manager is to make sure your team knows how to use it, trusts themselves to use it correctly, and never loses sight of the judgment that makes them irreplaceable in the first place. I’ve seen this quickly fail when someone tries to replace their daily work with AI slop, but it doesn’t have to be like that.
The managers who figure that out won’t just build faster teams. They’ll build better ones.
We’ve been here before
For anyone who’s been in management for a while, think back to early 2020, or even earlier for some of us. Suddenly we were managing teams we couldn’t see, running standups over video, trying to figure out how to maintain company culture across time zones and home offices. Nobody had a playbook. Many were skeptical it would work as well as being in the same room (some still don’t think it does).
When we figured out how to get the best out of people regardless of where they were doing the work, we got more intentional about communication. We built better documentation habits and processes. We learned to trust our teams in ways we had never imagined before. The teams adapted and didn’t just survive remote work; they got better because of what it demanded from them.
This is that moment again
AI isn’t going to manage your team for you, and it won’t write good code without creative minds behind it. AI won’t replace judgment, gut feelings, relationships, or leadership your team needs. It will, however, separate the teams that adapt from the ones that wait. This is the same as before, the transition is going to feel uncomfortable at first. Same as before, there’s no playbook that has all the answers. And same as before, the Engineering Teams who lean in, who treat it as a real challenge worth solving rather than a threat worth resisting, will come out on the other side as a stronger team than it was going in; a team that adapts and delivers the work demanded of an ever-evolving world.
We’ve already done this over and over. This is just the next iteration of the job.