Not long ago, one of our interns asked me during a coffee break, "Do you think AI means we’ll need fewer engineers in the future?"
I paused, thought for a second, then replied, "Maybe not fewer, but definitely different".
It wasn’t a deep conversation. But it stayed in my head. Because it reflected a bigger truth I’d been noticing across projects, planning meetings, and performance reviews: we’re not just shifting tasks, we’re shifting team dynamics.
We’re not just adding AI to our toolbox. We’re rewriting the assumptions about how many people you need to build and maintain software. It’s time to rethink how teams are shaped.
The Old Model: Scaling with People {#the-old-model:-scaling-with-people}
For decades, software teams scaled by headcount. More features? Hire more devs. More quality support? Add more QA. Need speed? Spin up a dedicated squad.
Most teams followed a familiar shape: a couple of senior engineers, a bunch of junior or mid-level engineers, and maybe a tech lead or architect steering the ship. The seniors made architectural decisions, reviewed merge requests, mentored the juniors, and unblocked problems. The juniors wrote a lot of the code.
It made sense. Coding was manual. You needed hands on keyboards.
But when AI can now scaffold entire features, write tests, and even summarize documentation, do you still need that same ratio?
AI isn’t replacing the team, but it’s reshaping it
AI won’t replace software engineers. But it will absolutely change what your team looks like and what each person is responsible for.
Here’s the emerging pattern I’ve seen:
- Instead of 2 senior and 4 juniors, you might have 2 seniors, 2 juniors, and AI doing the rest.
- The seniors aren’t just reviewers, they’re AI enablers. They guide the tooling, ensure quality, and stay responsible for long-term architecture.
- The juniors don’t write every line from scratch, they learn by prompting, reading, reviewing, and improving AI-generated output.
- The repetitive work? That’s AI’s job now.
In the example above, the team size was reduced by 33%, and it is real, we’ve already seen projects delivered faster, with better quality, and with fewer people when AI is integrated effectively into the workflow.
And the good thing is that it frees up the humans to focus on what humans are great at: judgment, creativity, and thinking ahead.
So, will AI take over all junior engineering jobs? No, not at all.
If we stop helping junior engineers grow, we’re giving up on the people who will become our future tech leads and problem solvers. AI can’t replace their potential to think deeply, solve complex issues, or make smart choices.
But what we expect from juniors is changing. We don’t need people just to write simple, repetitive code anymore. What we do need are juniors who are curious, who want to learn how to work with AI, who ask good questions, and who try to understand why a solution works, not just how to write it.
That means we need to change how we train them. Instead of just teaching syntax or how to follow instructions, we should help them understand how to think through problems, use AI tools wisely, and ask the right questions when something doesn’t make sense.
Their job is to learn, to get better with help from seniors, and to grow into engineers who know how to guide both code and AI tools in the right direction. The junior path is still here, but it’s not just about typing anymore. It’s about learning to think like an engineer in a new kind of team.
How to Rethink Your Team
If you’re leading a team or building one, here’s how to rethink your structure in the AI era:
Hire for Thinking, not Typing
Prioritize engineers who can evaluate trade-offs, guide AI tools, and adapt quickly.
It’s not about who can write the most lines of code, it’s who can make sense of AI’s output and steer it toward a solid solution.
Elevate Senior Engineers into AI Coaches
Your seniors should spend less time writing every piece of code, more time curating AI prompts, reviewing, mentoring, and setting standards.
Think of them as system architects and feedback loop maintainers.
Still invest in Juniors, but train them differently
Junior engineers still matter. But their job starts differently now. They’ll need to learn how to think through problems first, then use AI to accelerate solutions.
Pair them with seniors to develop judgment. Let them correct AI output, debug edge cases, and gradually own more responsibility.
Reduce role silos with shared context
In the past, PMs wrote specs, engineers wrote code, and docs were scattered across Slack, Confluence, Jira, or maybe Notion.
Now, AI works better when everything lives in the same place, code, decisions, product goals. Everyone needs to collaborate around a shared repo.
That means PMs might create merge requests. Engineers might write documentation alongside code. Context isn’t optional, it’s fuel for your AI stack.
Let AI be part of the team
Give AI real tasks: drafting specs, generating tests, rewriting code to follow conventions.
Review its work just like you would a new hire.
Build workflows where AI shows up in code reviews, planning sessions, and retros.
In the AI era, great teams will be:
- Smaller because AI handles the repetitive work.
- Smarter because senior engineers spend more time thinking, coaching, and shaping the system.
- More fluid with tighter collaboration across roles, centered around shared tools and context.
- More valuable because they ship faster, maintain quality, and still grow talent.
You don’t need more people to build more software. You need the right people, using AI the right way.
Let the tools do what they’re good at. Let your team focus on what really matters.