AI isn’t removing junior engineers, but it is changing what the role looks like, what people need to learn and how we support them. This is a good development.
, a real estate tech startup. He writes about engineering leadership, startup growth, scaling SaaS and AI.There’s been a lot of noise recently about how AI is eliminating the junior engineer—as if the role itself will disappear overnight.
The argument is, if any AI coding tool can fix bugs, write tests, refactor, build features and handle the chores we all hate, what’s left for someone who is just starting their career?AI isn’t removing junior engineers, but it is changing what the role looks like, what people need to learn, how we support them and what we should expect from them. This is a good development.In the past, junior engineers were onboarded to a new team or project by taking on safe, repetitive work such as chores, copy updates, configurations and cleanup tasks. The goal of this work wasn’t to drive impact for the business but to build context for the new engineer. The thinking was that a new engineer could best learn their new team's product, tools and conventions by touching small parts of the codebase over time, often with a shadow or onboarding partner. That type of work just doesn’t exist in the same way anymore. AI handles this work faster than any human can and with fewer mistakes. Keeping this work around “for training” is like making pilots learn to fly by hand-cranking the propeller. It’s nostalgia disguised as pedagogy. If your team is still creating this type of onboarding work for juniors, you’re doing them and your business a disservice. The challenge shouldn’t be finding small tasks to hand off. It should be finding real problems they can use to build context while working with your AI toolset. Tomorrow’s early-career engineers won't just be ticket takers; they’ll need to be systems-level thinkers. They’ll need to think about architecture and the intent of their work much earlier in their careers than they’ve had to in the past. Successful AI prompting depends on context. If you can’t explain the “why” behind your work, your AI partner will hallucinate and make nonsense decisions. To get real value out of AI, you need to understand how your system actually works: data flows, dependencies, architecture, trade-offs. That means that even as our tools have gotten more powerful than we could’ve imagined, the bar is going up, not down, for new engineers.Onboarding juniors used to mean giving them access to your repos and letting them “find their way” through structured tasks. That doesn’t work anymore. New engineers—and the AIs they use—need as much context as possible from day one. To support this, your teams should be building systems that make that possible: coding agent instructions that define tone, structure and coding conventions; AI-friendly documentation that clearly describes the architecture, purpose and dependencies of each service; and shared MCP configurations that bring your tools/systems into AI context and standardize your team’s access. This isn’t administrative overhead; it’s important leverage. When you make your business context accessible to AI, you also make it accessible to your team. So-called “AI reluctant” engineers are disappearing fast. The best unlock is hands-on experience—hesitation disappears as soon as they see what’s possible. Team AI training, workshops or even lightweight “AI office hours” can turn skeptics into advocates quickly. Once an engineer ships a feature twice as fast with AI, there’s no going back. And soon, new grads will enter the workforce already fluent. They’ll arrive AI-native with years of prompting experience, but with gaps in debugging, performance tuning or understanding what happens under the hood. That’s not a failure; it’s just a different starting point. Our job as engineering leaders is to understand their strengths but recognize the fundamentals that AI can’t replace.The traditional onboarding progression—small bug, small feature, bigger feature, etc—isn't relevant anymore. Instead of ramping up complexity, we should be focused on building context. We should bring juniors into the fold early so they can see how experienced engineers think and work—not just what they produce. Processes like design discussions, code reviews and postmortems should be exposed to them early, and all of the artifacts should be readily available to your AI tooling. If you’re already using AI heavily, consider doing “prompt reviews” to help train, alongside code reviews. Talk about what the AI got right, what it missed and why. That’s where real learning will happen. The worst thing we can do is turn junior engineers into prompt-operators. The best thing we can do is teach them to think critically about what AI produces and to use it as a force multiplier, not a crutch. The new generation will skip some of the slow, painful learning curves many of us went through. They’ll make mistakes we wouldn’t—and solve problems we couldn’t. They’ll be faster, bolder and more comfortable working with uncertainty. They’ll expect context to be structured, accessible and explainable. And they’ll push us to modernize how we work in the process. If that sounds uncomfortable, that’s the point. This isn’t the end of junior engineers. It’s the start of a much more interesting era for them. The only real mistake would be pretending nothing’s changed.
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