I never thought I'd say this as someone who has embraced AI from the beginning, but after relying on it heavily for an extended period of time, I realized something that caught me off guard:
I was becoming less capable of writing code on my own.
Artificial intelligence has fundamentally changed the way we build software, and I would be lying if I said it hasn't made me a more productive engineer. I use AI almost every day, and there is no denying that it can dramatically reduce the time it takes to solve problems, scaffold new features, explain unfamiliar concepts, or even uncover bugs that might have taken much longer to find on my own. For a while, it felt like I had discovered a superpower. The more I leaned into AI, the more productive I became. Features shipped faster, repetitive work disappeared, and I found myself spending less time searching through documentation or piecing together solutions manually.
Then I noticed something that I wasn't expecting.
The more I relied on AI to produce code, the less comfortable I became producing it myself. I hadn't forgotten how to program, but I had unknowingly started disconnecting myself from the work. Instead of sitting with a problem long enough to understand it deeply, I found myself asking AI to generate the solution almost immediately. Instead of tracing through code to understand why something worked, I accepted explanations that were already synthesized for me. Even when debugging, my first instinct became asking AI where the issue might be instead of methodically walking through the application myself. None of these decisions seemed harmful in isolation, but over time they accumulated into a habit that slowly eroded the very skills I had spent years developing.
What surprised me most was that my productivity metrics never reflected this change. From the outside, I looked more efficient than ever. I was completing tasks quickly, delivering features on time, and producing quality code. But internally, I realized that I wasn't exercising the same engineering muscles that had gotten me to this point in my career. I was becoming exceptionally good at collaborating with AI, while becoming less practiced at independently designing, implementing, and reasoning through complex systems. The work was still getting done, but I wasn't always the one doing the hard thinking.
That realization forced me to reconsider what software engineering actually is. Writing code has never been the hardest part of the job. The real challenge has always been understanding systems, evaluating tradeoffs, recognizing patterns, and making decisions that continue to make sense months or even years later. Those skills are built through experience, repetition, and sometimes frustration. They come from spending hours trying to understand why something behaves the way it does, reading through unfamiliar codebases, making mistakes, and learning from them. AI is remarkably good at removing much of that friction, but that friction is often where the learning happens.
This becomes even more important when working on a team. Every engineer has a responsibility that extends far beyond simply getting a feature across the finish line. Eventually, someone else will need to understand the code you wrote. That person might be another developer joining the project, a teammate maintaining your work, or even you six months later when you've forgotten the details. If most of your implementation came from conversations with an AI model rather than from your own understanding of the system, you may find yourself struggling to explain why certain decisions were made or where changes should be introduced. Ownership isn't just about writing code; it's about understanding it well enough to confidently maintain it, extend it, and teach it to someone else.
None of this is an argument against using AI. In fact, I believe AI is one of the most transformative tools our industry has ever seen, and ignoring it would be just as shortsighted as becoming completely dependent on it. The issue isn't the technology itself but how we choose to integrate it into our workflow. AI should accelerate our thinking, not replace it. It should help us validate ideas, challenge assumptions, automate repetitive tasks, and expose us to approaches we might not have considered. What it shouldn't become is our default source of answers before we've given ourselves the opportunity to wrestle with the problem first.
Over the past month, I've intentionally changed the way I work. When I'm faced with a new problem, I try to solve it on my own before opening ChatGPT or asking an AI agent for help. If I'm learning a new framework or library, I spend time reading the documentation and experimenting before asking AI to summarize it for me. When debugging, I force myself to investigate the issue long enough to develop my own hypothesis before comparing it against AI's suggestions. Ironically, I've found that AI becomes significantly more valuable when I already understand the problem because I can critically evaluate its recommendations instead of simply accepting whatever it generates.
I also think this has implications for engineering teams as a whole. As AI agents become increasingly capable of writing large portions of applications, there is a temptation to optimize almost exclusively for delivery speed. While shipping software quickly is important, it cannot come at the expense of engineers understanding the systems they are responsible for maintaining. Teams that become overly dependent on AI-generated implementations risk creating knowledge gaps that don't become obvious until months later, when new features need to be built, critical bugs emerge, or key contributors move on. A team's long-term strength isn't measured only by how quickly it can produce code today but by how deeply its engineers understand the systems they will be responsible for tomorrow.
I've come to believe that the healthiest relationship with AI is one of intentional restraint. Use it to eliminate repetitive work.
- Use it to review your ideas.
- Use it to challenge your assumptions.
- Use it to become faster after you've done the thinking.
But don't let it replace the very process that develops your judgment as an engineer. The goal isn't simply to produce software as efficiently as possible. It's to become the kind of engineer who can confidently build, maintain, explain, and improve that software long after the AI conversation has been closed.
Artificial intelligence has undoubtedly made me faster, but it also taught me an unexpected lesson. Productivity and growth are not always the same thing. Sometimes the struggle we are trying to eliminate is the very thing that makes us better. As AI continues to evolve, I believe one of the most important skills we can develop isn't learning how to ask better prompts, it's knowing when NOT to ask at all.
👍 Happy Coding...
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