March 17, 2025
Software development is changing fast, and AI is playing a massive role in how junior developers learn. With tools like Copilot, GPT, and Claude, they’re shipping code at lightning speed. But when you ask them why their code works or what happens in edge cases, the responses are worrying.
The struggle that once built strong problem-solving skills is disappearing. Quick AI fixes are replacing deep understanding, and we’re heading for long-term consequences.
So, what can we do to fix it? Let’s break it down.
I remember when I first started coding—StackOverflow was my best friend. I’d spend hours debugging, searching for answers, and learning from discussions. Every problem I solved was a step toward becoming a better developer. But today, things are different. Junior developers have Copilot, ChatGPT, and Claude writing entire functions for them in seconds. It’s impressive, but there’s a catch.
AI tools have become essential in modern coding, making it easy to churn out working solutions without understanding the underlying concepts. Sure, AI speeds up coding, but does it improve actual understanding? Not really. Instead of struggling through logic, many juniors now copy and paste AI-generated solutions, skipping the learning process altogether. The ability to troubleshoot, optimize, and truly comprehend code is fading. And that’s a problem.
Back in my university days, students would often copy assignments from their peers. But there was a catch—you had to tweak the code enough to bypass duplicate-checking software. In doing so, you unintentionally learned something, even if it was just a small concept or debugging trick.
Fast forward to today, and things have changed. Instead of borrowing code from a friend and making adjustments, students now just paste the problem statement into ChatGPT or Copilot and get a fully functional solution in seconds. No need to think, no need to debug—just copy, paste, submit. This means many fresh computer science and software engineering graduates enter the workforce with little real coding experience.
I’ve seen this firsthand in my company. A junior developer joined our team and heavily relied on AI-generated code. At first, it seemed efficient, but then I noticed a worrying trend—he didn’t even bother to test his code thoroughly. As long as the happy path worked, he considered it done. But when the code hit production, it frequently failed in edge cases. One day, ChatGPT went down, and he was completely stuck. He couldn’t write a single function from scratch.
Even worse, when we encouraged him to dive deeper and truly understand the code, he resisted.
"Why should I struggle to learn everything deeply when AI can do it for me? And what if AI takes my job in the future anyway?"
This fear of deep learning is becoming more common. Many junior developers are hesitant to invest time in understanding code because they believe AI will handle everything. But without foundational knowledge, they won’t be able to adapt when AI-generated solutions fail—or when they need to build something AI doesn’t understand yet.
I remember when StackOverflow was the go-to place for developers. You’d post a question, and within hours (or sometimes minutes), experienced developers would chime in with different solutions, explanations, and best practices. The beauty of StackOverflow wasn’t just in the answers—it was in the discussions. You’d see different approaches, debates on performance optimizations, and explanations that helped you understand the deeper aspects of programming.
With AI, that dynamic has changed. Now, developers simply type their problem into ChatGPT or Copilot, and boom—instant answer. No discussions, no community feedback, just a straight solution. While this sounds great for productivity, it removes the human element of learning. AI doesn’t question its own answers the way experienced developers do. It doesn’t challenge assumptions or provide context on why one approach might be better than another.
I’ve seen new developers completely bypass StackOverflow because they can get quick AI-generated code. But when you look at their work, it’s often missing efficiency, best practices, or even basic readability. They’re missing out on the learning process that comes from reading and understanding different perspectives.
StackOverflow encouraged deep thinking and problem-solving, while AI offers quick fixes. The question is—how do we strike the right balance between speed and learning?
Instead of blindly accepting AI-generated code, developers need to approach it with curiosity. When AI provides a solution, ask:
Experiment with tweaking the code, breaking it, and understanding the reasoning behind each line. The goal is to use AI as an assistant, not a crutch.
Back in the day, learning came from interacting with other developers—discussing problems, debating solutions, and sharing insights. That’s still valuable today. Platforms like Reddit, Discord, and Mastodon host thriving dev communities where people discuss challenges and exchange ideas. Participating in these discussions can provide a deeper understanding of coding principles beyond what AI can offer.
Code reviews should go beyond "Does this work?" to "Why was this approach chosen?" Encouraging team discussions around different solutions helps junior developers think critically. Instead of just approving a pull request, reviewers should challenge assumptions, ask for alternative implementations, and ensure that AI-generated code aligns with best practices.
One of the best ways to learn is by struggling through a problem without AI. Encourage junior developers to first attempt a feature manually before turning to AI for optimization. Writing code from scratch helps build problem-solving skills, debugging abilities, and a deeper understanding of how things work.
AI is here to stay, but the best developers will be those who know how to leverage it wisely. The key is finding the right balance between using AI for efficiency and ensuring that deep coding knowledge isn’t lost. If we don’t actively foster real learning, we’ll end up with a generation of developers who can ship code but not truly understand it.
So, let’s be intentional. Use AI, but challenge it. Engage with the dev community. Write bad code before optimizing with AI. That’s how we keep software development strong.