My Love-Hate Relationship with AI in Coding
I love using AI for coding. It helps me write scripts faster, debug issues quicker, and even learn new programming concepts by studying the code it generates.
But here’s the thing: AI isn’t perfect. And if we rely on it too much especially in production or client-facing work—we risk serious problems.
In this post, I’ll share my personal experience with AI in coding, the risks I’ve seen, and how I balance AI assistance with my own skills. If you’re a developer, a tech lead, or just someone curious about AI’s role in programming, this is for you.
Why AI in Coding is a Game-Changer
Before diving into the risks, let’s talk about the benefits. AI has transformed how I work in several ways:
Faster Prototyping – Need a quick script to test an idea? AI can generate it in seconds.
Automating Repetitive Tasks – Writing boilerplate code? AI handles it, so I can focus on logic.
Learning New Concepts – AI can explain complex topics in simple terms, making it easier to pick up new languages or frameworks.
Debugging Help – Stuck on a bug? AI can suggest fixes or point out where things might be going wrong.
For me, AI is like having a junior developer who never sleeps—always ready to assist, but still needing guidance.
The Risks of Over-Reliance on AI in Coding
But—and this is a big but—AI isn’t a magic solution. Here are the real risks I’ve encountered (and why you should be careful too).
1. The "Black Box" Problem: You Don’t Always Know What’s Inside
AI-generated code can work perfectly… until it doesn’t.
I’ve seen cases where AI introduces subtle bugs or inefficiencies that only appear under specific conditions. Since AI doesn’t explain why it wrote the code a certain way, debugging becomes a nightmare.
Example:
Once, an AI-generated function worked fine in testing but failed in production because it didn’t handle edge cases properly. It took hours to trace back and fix—time I could have saved if I’d written it myself with full understanding.
Lesson: Always review AI-generated code like you would a junior developer’s work. Don’t assume it’s perfect.
2. Over-Reliance Erodes Your Coding Skills
If you let AI write all your code, you risk losing your ability to think critically.
Coding isn’t just about typing syntax—it’s about:
- Understanding systems
- Anticipating edge cases
- Making smart trade-offs
AI can’t replace that human intuition—at least, not yet.
Example:
Early in my career, I relied too much on copy-pasting Stack Overflow answers. Later, I realized I hadn’t really learned how to solve problems—I’d just memorized solutions. AI can create the same issue if we’re not careful.
Lesson: Use AI as a learning tool, not a crutch. Study the code it generates, understand why it works, and improve it.
3. Client Trust is on the Line
In client work, reliability is everything. If an AI-generated solution fails, it’s your reputation that suffers—not the AI’s.
Clients don’t care how the code was written—they care that it works every time.
Example:
A colleague once used AI to generate a payment processing script. It worked in tests but failed in production, causing delays and frustration. The client didn’t blame the AI—they blamed us.
Lesson: Never ship AI-generated code without thorough testing. Treat it like unproven third-party code.
4. Ethical and Security Blind Spots
AI doesn’t always follow best security practices or consider ethical implications.
I’ve seen AI suggest:
- Insecure shortcuts (like hardcoding passwords)
- Inefficient algorithms (that slow down performance)
- Biased logic (if trained on flawed data)
As developers, we’re responsible for catching these issues.
Example:
An AI once suggested a database query that was vulnerable to SQL injection. If I hadn’t reviewed it, that could have been a disaster.
Lesson: Always audit AI-generated code for security and ethics.
My Approach: Using AI as a Collaborator, Not a Replacement
So how do I use AI without losing my skills or risking mistakes? Here’s my strategy:
1. Treat AI Like a Junior Developer
- It’s fast and helpful, but needs supervision.
- Always review, test, and refine its output.
2. Use AI for Brainstorming, Not Final Code
- Great for exploring ideas or generating drafts.
- But I never use raw AI code in production without changes.
3. Study the Code It Generates
- If AI suggests a solution, I ask: Why does this work?
- This helps me learn and improve my own skills.
4. Keep My Core Skills Sharp
- I still write code from scratch sometimes.
- I solve problems manually before asking AI for help.
The Bottom Line: AI is a Tool, Not a Replacement
AI is incredibly powerful, but it’s not a substitute for:
✔ Human judgment
✔ Critical thinking
✔ Experience and expertise
The best developers won’t be those who rely most on AI—they’ll be the ones who use it wisely while keeping their own skills sharp.
Final Thought: What’s Your Approach?
How do you use AI in coding? Do you see it as a helper, a risk, or something in between? Let’s discuss in the comments!
About the Author
Diamantino Almeida is a tech leader, coach, and writer reshaping how we think about leadership in a burnout-driven world. With over 20 years at the intersection of engineering, DevOps, and team culture, he helps humans lead consciously from the inside out. When he’s not challenging outdated norms, he’s plotting how to make work more human one verb at a time.
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