After more than a decade in web development, I’ve always taken pride in how seriously I approach code. I used to spend time reviewing every function, optimizing queries, and making sure everything worked efficiently. Writing clean and reliable code wasn’t just part of the job—it was something I genuinely cared about.
But recently, I caught myself slipping.
And AI had a lot to do with it.
The Temptation of AI-Assisted Coding
AI coding tools are incredibly powerful. They can generate functions, suggest solutions, debug errors, and write large portions of code almost instantly. What used to take time and careful thinking can now appear in seconds.
At first, it felt like a superpower.
Instead of writing everything manually, I started relying more and more on AI suggestions. Tasks moved faster, features were implemented quickly, and development felt smoother.
But over time, something subtle happened.
I started trusting the AI too much.
When Convenience Turns Into Complacency
On one of my WordPress projects, I allowed AI to generate and modify large portions of the code. Instead of reviewing everything carefully like I used to, I began accepting the output more casually.
If the code worked and the feature looked fine, I moved on.
Looking back, I realized I had started vibe coding—letting AI drive most of the programming while I simply guided the direction.
At the time, everything seemed fine.
Until it wasn’t.
The Moment I Realized the Problem
I only realized there was an issue when my superior informed me that the website’s performance had degraded.
It caught me off guard.
From my perspective, the site was functioning. But once I started digging deeper, I began to see what had actually happened.
The problem wasn’t a single bug.
It was a series of small inefficiencies—unoptimized queries, unnecessary logic, and code that worked but wasn’t written with performance in mind.
Individually, they didn’t look serious. But together, they slowed the system down.
And that moment became a real eye opener for me.
The Real Lesson
The problem wasn’t AI.
The problem was my complacency.
AI had made it easy for me to generate code quickly, but I stopped applying the same level of discipline and scrutiny that I used to.
Before AI, writing code required effort. That effort naturally forced developers to think carefully about structure, performance, and long-term maintainability.
With AI, that friction disappears.
And when the friction disappears, developers must become even more disciplined—not less.
Moving Forward
That experience reminded me of something important: AI should be an assistant, not an autopilot.
It can help generate ideas and accelerate development, but the responsibility for the final code still belongs to the developer.
Now when I use AI tools, I treat the generated code as a starting point, not a finished solution. I review it carefully, understand what it’s doing, and make sure it aligns with good development practices.
Because at the end of the day, no matter how powerful AI becomes, the developer is still responsible for the quality of the work.
And sometimes, it takes a wake-up call to remind us of that.
