The Dangerous Dependence: Why Relying Too Much on AI is Making New Developers Weaker
AI in development is here to stay. GitHub Copilot, OpenAI’s Codex, and similar tools have become popular shortcuts, helping developers code faster and catch bugs. But here’s the reality: while these tools promise convenience, they also risk making developers lazy and weakening the foundation of their skills.
Shortcuts Don’t Build Skills
AI tools are great at speeding up certain tasks—write some code, get some suggestions, rinse and repeat. But relying on AI too much doesn’t build the problem-solving muscle every developer needs. Coding isn’t just about writing code; it’s about analyzing problems, breaking them down, and finding solutions. When you let AI handle the bulk of the work, you bypass this critical process.
If you only focus on what AI spits out, you’re not learning how to truly solve problems. At best, you’re just copying someone else’s work. At worst, you’re neglecting the skills that make you a strong developer.
The Core Understanding Is Fading Away
When you lean too heavily on AI to write your code, you’re skipping over foundational concepts like algorithms, data structures, and design patterns. These principles form the backbone of good coding practices, and without a solid understanding of why things work, your solution is just a quick fix, not a lasting one.
Here’s the thing: when problems inevitably arise, and they will, you won’t have the depth of knowledge to fix them effectively. AI can get you part of the way, but when things get messy, you’ll be left without the expertise to adapt, optimize, or even understand what’s broken.
Debugging: The Lost Art
Debugging is where the real magic happens in coding. It’s where you learn how things fail and how to fix them. But here’s the problem: if you’re just running code generated by AI, you’re skipping this crucial step. AI might give you a solution, but it doesn’t teach you how to troubleshoot and trace problems back to their root cause.
A key skill in development is debugging—and you can’t develop that skill if you never have to work through an issue yourself. Debugging is an essential part of growing as a developer. You can’t just let AI do it for you.
AI Can Help You Write Code, But It Won’t Teach You How to Work With a Team
AI is great for writing code quickly, but in the real world, development isn’t just about coding alone—it’s about working as part of a team. And AI doesn’t help with that. Tools like GitHub Copilot can generate snippets, but they don’t teach you how to collaborate with others, handle feedback, or manage workflows.
Here’s the reality: software development is a team sport. Whether you’re coordinating with other developers, aligning with project managers, or working with designers, you need to be able to work with others. AI can provide solutions, but it doesn’t teach you how to integrate your work with others, how to navigate code reviews, or how to manage pull requests.
AI Doesn’t Teach Team Collaboration
In real-world development, you’re almost never working alone. Every piece of code you write impacts the rest of the team’s work. Using AI to generate code for a feature you’ve been assigned to can be helpful and fast, but unless you’ve downloaded the entire codebase and have communicated the global coding standards to the AI, the generated code may work, but it won’t necessarily align with the team’s current standards or vision.
AI tools can provide a quick solution, but they won’t understand the nuances of your team’s coding practices or the larger architectural decisions your team has made.
Pull Requests (PRs): More Than Just Code
Writing code is only part of the process. Enter Pull Requests (PRs). PRs are essential for collaboration, as they ensure that your code aligns with the overall project goals. But AI doesn’t teach you how to structure a PR, respond to feedback, or handle disagreements during the review process.
If all you focus on is writing code, you miss out on learning how to handle PRs effectively. Knowing how to present your work, handle criticism, and integrate feedback is what sets you apart as a strong developer. AI can’t teach you how to work through these interactions.
Code Reviews: The Art of Quality Assurance
Then, there’s code review—an essential part of the development process. AI might suggest fixes, but it can’t evaluate the quality of your code in the context of the entire project. Code reviews are about more than just fixing bugs; they’re about ensuring your code meets the team’s standards and aligns with the project’s architecture.
How you respond to code reviews is critical. Can you explain your design decisions? Can you take constructive criticism and improve your code accordingly? These are real-world skills that AI can’t provide, and if you can’t navigate them, you’ll struggle in collaborative environments.
The AI-Enhanced Approach: Break Tasks Down, Don’t Let AI Take Over
In our company, we encourage using AI tools to help our developers and QA teams. But we don’t let AI take over. We advise our team to use AI as a reference or an alternative, not a replacement.
Here’s the rule: break tasks into smaller chunks. If you’re working on a big feature, don’t let AI rewrite the entire branch. Letting AI take over entire codebases or test cases can lead to massive issues when those changes are accidentally merged into the main or prod branches.
When AI tools are used correctly, they make things more efficient. But they shouldn’t replace the core skills you need as a developer. The key is to manage the tools so they don’t manage you.
The AI Developer: Focus on Learning, Not Just Code
At the end of the day, I’ll take a developer with limited coding experience but a strong ability to learn over someone who’s just quick with AI tools. Developers who have less experience but a solid learning capability tend to excel at analyzing code, breaking down problems, and understanding the root cause of issues. They don’t rely on shortcuts—they focus on learning and improving.
A developer with a strong learning mindset doesn’t just copy code from AI—they dig into the code, learn from it, and apply that knowledge. This is the kind of developer who will thrive in real-world projects, where problems are unpredictable and require creative solutions. Learning is a skill that AI can’t replace.
The Ugly Truth: Real Projects Aren’t Predictable
Real-world software projects are messy. Bugs happen, things break, and timelines shift. Sure, with experience, things can get easier, but don’t kid yourself—complex issues can crop up at any time. If you don’t have the solid skills to handle those challenges, it will spiral quickly. Fast.
AI tools can be incredibly helpful, but they shouldn’t be the foundation of your development skills. Developers who rely too much on AI are setting themselves up for failure. Instead of blindly trusting AI, learn to understand your code, why things work, and how they break. You’ll be a better developer in the long run.
Conclusion: Why We Do What We Do
As more teams and companies dive into AI-driven development, it’s important to recognize the power of these tools while also understanding their limitations. At our company, we’ve chosen to embrace AI as an accelerator—helping our developers and QA teams be more productive and efficient. But we’re also mindful that AI can’t replace core development skills like problem-solving, debugging, and collaboration.
AI tools are great for speeding up tasks, but they don’t have the context or understanding of your team’s standards, goals, or challenges. That’s why we emphasize breaking down tasks, collaborating closely, and continually learning. These practices ensure that our developers remain adaptable and equipped to handle the unpredictable challenges of real-world projects, even when AI is doing some of the heavy lifting.
By combining AI’s capabilities with strong foundational skills, we ensure our teams stay sharp, collaborative, and focused on long-term growth. AI is here to help, but it’s the human touch that makes the difference.