Introduction
Last week, I had a moment where GitHub Copilot felt like a more rational choice than delegating work to a junior developer. I am slightly embarrassed to admit it, yet I still feel like it was the right call. But why exactly is that?
My task was to refactor a fairly complex API endpoint. The difficult part wasn't the implementation itself, but rather understanding the interactions between all involved systems and breaking down the work into manageable steps. After a few hours of anlysis, I had created a farily straightforward step-by-step implementation plan.
Seconds later, I caught myself asking: "Do I give the implementation work to the junior developer, or do I give it to Copilot?" I chose Copilot. And while it felt like the more efficient choice, it kept bothering me afterwards.
A few years ago, many of these implementation tasks would have gone straight to junior developers:
- Adding CRUD endpoints
- Generating unit tests
- Refactoring straightforward code
- Debugging simple errors
- Documenting APIs or services
Today, many senior engineers delegate these tasks to GitHub Copilot. And honestly, in many cases that actually IS the more efficient choice. But it raises a deeper question: If AI is increasingly becoming better at execution-heavy development tasks, what exactly are junior developers supposed to learn from?
Where AI Coding Tools Already Outperform Traditional Delegation
Before we dig into this particular question, let's first look at what AI currently excels at.
When it comes to software development, modern AI coding tools like GitHub Copilot are extremely good at certain types of work. In particular, they excel at:
- Pattern recognition
- Syntax generation
- Boilerplate production
- Code transformation
This is especially true if requirements are clear, ambiguity is low and correctness is easy to verify.
What all of those tasks have in common is that they primarily focus on execution and require relatively little organizational context or domain knowledge. When dealing with implementation responsibilities, AI tools like GitHub Copilot have several structural advantages over junior developers:
- They are faster
- Have zero onboarding cost
- Work instantly
- Scale cheaply
- Handle repetitive work effectively
- Reduce human errors to zero
For senior engineers, this immediately translates into less delegation overhead, fewer interruptions, shorter review cycles, and ultimately faster delivery. In other words: AI significantly increases developer productivity for certain categories of implementation tasks.
And that raises an uncomfortable question: If AI is already better at execution-heavy development tasks, why not give every task to AI? That's why many, especially junior developers, are afraid to get replaced by AI coding tools.
Why Task Purpose Matters More Than the AI vs. Junior Developer Decision
The problem with the "AI vs. junior developer" question is that it assumes all software development tasks are the same. In reality though, they are not.
Over the years, while working as a software engineer, mentoring junior developers, and leading whole projects, I’ve learned that most engineering tasks fall into three broad categories:
1. Tasks that require ownership
The first category of tasks is tasks that require someone to take long-term responsibility for the system and its architecture.
Examples are:
- Designing system architecture
- Making security-critical decisions
- Handling production incidents
- Evaluating technical tradeoffs
- Maintaining long-term system consistency
- Coordinating cross-team integrations
These tasks usually require a deep understanding of the system, architectural judgement, and a solid foundation of the business context.
People working on them often need to evaluate tradeoffs, make long-term technical decisions with impactful consequences and coordinate requirements and decisions across multiple systems and teams.
In other words, these tasks come with real ownership and responsibility. That is exactly why they cannot simply be delegated to an AI, or handled independently by junior developers.
2. Tasks that enable fast software delivery
We've already touched on these tasks earlier, but let's make the category more explicit.
First, let's go over the examples again:
- Generating repetitive CRUD endpoints
- Writing straightforward unit tests
- Transforming data between APIs
- Scaffolding new services/modules
In my experience, tasks that enable fast delivery of software usually involve clear requirements, repetitive implementation patterns, low ambiguity and are easy to verify.
People working on them typically need to follow well established patterns to translate well-understood requirements into working code effectively and quickly.
These are exactly the kind of tasks where AI coding assistants like GitHub Copilot already outperform developers writing code on their own in terms of speed and efficiency.
3. Tasks that help people grow
This brings us to the third category: tasks that are intentionally delegated as learning opportunities.
Examples are:
- Implementing a small feature end-to-end
- Debugging a non-critical bug
- Refactoring legacy code to learn architecture
- Writing tests for unfamiliar business logic
- Participating in code reviews
These tasks usually involve low levels of complexity, rudimentary domain knowledge, touch only non-critical code, and leave room for exploration.
People working on these tasks typically need mentorship, fast feedback loops, and enough time to not only understand the problem, but also why the solution is built that.
In the age of AI, tasks that are not primarily focused on speed or efficiency become even more important. Future senior developers still need opportunities to grow through real-world experience and take on more responsibility.
Where Junior Developers Still Matter
Up until this point, we've looked at the problem from a productivity perspective, focusing on the task-level. But the problem becomes even more visible once we start thinking on an organizational-level, where software engineering teams must make strategic choices about AI adoption.
Growing Future Senior Engineers
In any engineering organization, junior developers should not be seen only as current contributors, but as future senior engineers. Therefore, it's the responsibility of software engineering teams to enable growth and help them learn.
To accomplish that, organizations need to create learning opportunities. From my personal experience working with many junior developers, I've found that they learn best and gradually become mid-level and senior developers by working on real-world problems and taking on increasing responsibility and accountability.
Teams therefore need to support them by allowing juniors to make mistakes and by providing constructive feedback in short feedback loops.
Without those opportunities, teams sacrifice long-term engineering capacity for short-term productivity.
System and Domain Knowledge Compounds
Another argument why organizations need to create learning opportunities for junior developers is that, over time, developers accumulate something that is difficult to document and impossible to generate instantly: system knowledge.
Developers learn why systems were designed a certain way, what tradeoffs were made, how systems behave under production-like conditions, which parts of the system are fragile, and where complexity exists in the codebase.
Besides a deeper technical understanding, developers also accumulate knowledge about the business domain itself.
They learn why certain features matter more than others, which edge cases are business-critical, how real-world users interact with the system, and where technical decisions directly affect business outcomes and vice versa.
This technical and domain expertise is built through years of working on real-world problems, participating in domain discussions, and solving real production incidents. Without gradually exposing junior developers to those experiences, organizations slowly but surely lose the capability to deeply understand their own systems.
Software Needs Long-Term Maintainers
But understanding a system is only one part of the equation. Somebody also needs to maintain it long-term. In practice, systems need to be maintained, debugged, extended, migrated, and sometimes even completely rewritten later.
That’s why I think organizations need more people who can just generate code fast, they also need people who continuously maintain the system.
Investing in Future Engineering Capability
The goal for every organization should therefore not be to replace junior developers with AI tools like GitHub Copilot, but rather to strategically use them alongside human developers. Sustainable software development still depends on continuously developing juniors into engineers who understand, maintain, and eventually take ownership of complex systems.
A Better Delegation Question
In software development teams, the rise of AI coding assistants like Copilot has made delegation decisions more complex. Tasks are often evaluated mostly based on delivery speed, but sometimes the biggest long-term benefit is not the finished feature—it's the knowledge the person gained while implementing it. A junior developer working on a feature may gain architectural understanding, domain knowledge, and practical experience that become valuable far beyond the task itself.
Every delegation decision also shapes the capabilities of the engineering team, and even the organization itself. Teams that only optimize for short-term efficiency may ship software faster today, but they sacrifice the ability to develop future maintainers, domain experts, and system owners.
I believe this ultimately changes the question that modern software engineering teams should be asking. Instead of rushing into deciding whether a task should go to AI or junior developers, engineering organizations should first ask what the task is actually meant to achieve.
Is the task about speed -> give it to AI
Is the task about teaching -> give it to a junior developer
Is the task about ownership -> give it to a senior and include a junior developer
Conclusion
So, should you give the task to AI or to a junior developer?
In my opinion, the answer depends less on the complexity of the task itself and more on what the task is actually meant to achieve.
The future of software engineering will not belong to teams that replace developers with AI, but to teams that understand how to combine speed, learning, and ownership effectively.
AI may change how software gets written, but organizations will still succeed or fail based on how well they develop people. The real competitive advantage in the age of AI will not be to generate code faster, but to build teams that continuously grow people who can understand, maintain and eventually own complex systems. And that is exactly why junior developers still matter.
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