Artificial Intelligence has exploded into the developer workflow. Tools like ChatGPT, GitHub Copilot, Tabnine, Codeium, Replit Ghostwriter, Amazon CodeWhisperer, and Sourcegraph Cody can now generate code, write documentation, suggest bug fixes, and even spark new ideas when you’re stuck.
As a developer, I use AI almost every day. But here’s the catch: I don’t fully trust it. Too often it produces answers that look correct but are completely wrong. If you blindly accept them, you risk bad code, wasted time, or even security vulnerabilities.
And yet, despite these flaws, I keep coming back to it. Why? Because if used wisely, AI can provide massive advantages.
Why I Use AI as a Developer
Rapid Prototyping and Boilerplates
Spinning up a new project used to take hours. Now AI generates a solid foundation in minutes. It’s rarely perfect, but it gets me 70–80% of the way there — a huge time-saver.
A Thinking Partner
When I’m stuck, AI acts like a “rubber duck that talks back.” It suggests directions I may not have considered, and while I don’t always take its advice, it pushes me forward.
Documentation (the boring part, automated)
Most developers dislike writing docs. AI can draft READMEs, inline comments, or summaries. I still rewrite them, but starting from a draft is so much easier than starting from scratch.
Instant Feedback — a “Mini Mentor”
For junior developers, AI is like a mentor on demand. It reviews code instantly, points out mistakes, and suggests improvements. It doesn’t replace a seasoned colleague, but it accelerates the learning curve.
Why I Don’t Fully Trust AI
Confidently Wrong
AI often hallucinates — inventing functions, APIs, or solutions that don’t exist. The code may look polished but collapses at runtime. This “confidence without correctness” is dangerous.
Over-Reliance
If we outsource too much to AI, we risk losing critical thinking. Coding is more than typing — it’s about architecture, design, and problem-solving. Those skills fade if we let AI do everything.
Data Privacy Concerns
There’s another reason I hesitate: the data.
Every prompt, every code snippet, every question is valuable information. Global AI companies may directly or indirectly use it to train their models.
To be honest, I’m not comfortable with that:
- I don’t know how my prompts are stored or analyzed.
- I don’t know if they could resurface later in unexpected ways.
- I don’t like the idea of contributing free data to make someone else’s product better, especially when it includes my own work or sensitive company code.
For many developers, these data privacy issues are a real dealbreaker — and I share that hesitation.
Ethical and Business Questions
AI-generated code also raises broader concerns:
- Who owns the copyright of AI-produced code?
- What if sensitive company data leaks into a model?
- Can businesses really trust mission-critical logic to a black-box AI?
There are still no clear answers.
How I Choose to Approach AI
I see AI as a partner, not a replacement. It’s like an enthusiastic junior developer: lots of ideas, sometimes brilliant, often off the mark. You’d never deploy a junior’s code without review — and the same goes for AI.
Developers who only “type code” may fall behind. But those who think in systems, ask the right questions, and solve real business problems will thrive with AI in their toolbox.
Final Thoughts
I use AI every day — but I always double-check what it produces. For me, AI is not an enemy but a tool: it makes me faster, more creative, and more productive — as long as I keep my critical eye open.
The key is balance: AI accelerates the work, but it doesn’t replace judgment.
What about you?
- Do you trust AI with code generation, or do you always review it?
- Is it more of a helpful partner, or an unreliable coworker you constantly have to double-check?
🙏 Thanks for reading,
Marxon
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