The benefits of AI in software development can't be overstated.
It's one of the most useful tools developers have today. It helps us automate repetitive tasks, generate boilerplate code, explain unfamiliar concepts, and dramatically speed up development. In the hands of a good engineer, AI can vastly improve workflows.
But while we're chasing raw speed, I think there's something critical we should be careful not to lose.
If used carelessly, AI can also make developers more dangerous.
What Do I Mean by That?
When I say AI has made developers more dangerous, I don't mean AI itself is bad. I mean that AI makes it entirely too easy to ship code we don't fully understand.
Instead of spending time thinking through a problem, exploring different approaches, and learning why a solution works, it's now possible to generate hundreds of lines of code in seconds. That speed is impressive, but it comes with a hidden cost.
When we write code ourselves, we're naturally forced to think about the system, its architecture, its edge cases, and the trade-offs involved. That thinking process is where learning actually happens.
When an AI generates the implementation for us, it's incredibly easy to review it too quickly, hit accept, and move on.
The more code AI writes for us without us truly understanding it, the harder it becomes to reason about the codebase later. Developers may end up shipping software that is increasingly harder to maintain, debug, and secure.
This is especially concerning for junior developers. If AI solves every problem before they've had the chance to struggle through it themselves, how do they develop the foundational problem-solving skills that engineering depends on?
As Richard Hamming once famously said:
"The purpose of computing is insight, not numbers."
I think the exact same applies to AI. The goal isn't just generating lines of code. The goal is understanding the solution.
How Do We Fix This?
I don't think the solution is using less AI. I think it's using AI differently.
Every line of code an AI generates should still explicitly become your code. That means you need to:
- Read it thoroughly.
- Question its constraints.
- Refactor it to fit your style.
- Test its boundaries.
- Understand exactly why it works.
Challenge the decisions the model made. Research alternatives. Never accept an answer simply because the AI sounds confident.
A workflow I've personally come to like is:
Think → Generate → Review → Refactor → Test → Repeat
This approach might feel slower in the moment. Ironically, I think it's actually much faster in the long run. You spend significantly less time debugging mysterious production problems later because you already deeply understand the code you're working with.
AI should remove repetitive work. It shouldn't remove your thinking.
Conclusion
AI isn't dangerous. The uncritical, blind use of AI is.
Like every powerful tool, its ultimate value depends entirely on the person using it. If we let AI think for us, we'll gradually lose one of the most valuable skills we have as engineers: problem-solving.
But if we keep ourselves firmly at the center of the engineering process—treating AI as a capable assistant rather than a replacement—we can build better software while continuing to grow as developers.
"The best tool is the one that makes you better, not the one that makes you stop thinking."
💭 This is just my perspective, and I'd genuinely love to hear yours.
Has AI helped you become a better engineer, or do you think it's becoming too easy to rely on it as a crutch? Let's discuss in the comments!
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