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Sayyad Arsalan
Sayyad Arsalan

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The Hidden Costs of AI-Powered Code Assistants

Hey, fellow devs! Let’s talk about something that’s been buzzing in our world lately—AI-powered code assistants like GitHub Copilot and ChatGPT. Cool, right? They’re helping us crank out code faster than ever. But hold up, are they all that great? Let’s dig deeper.

The Dependency Trap

Here’s the thing: these tools make life easier, but at what cost? If you’re always leaning on AI to spit out answers, how much are you really learning? Think about it—do you want to be the dev who knows the why behind the code or the one just copy-pasting? The more you depend on AI, the less you’re sharpening your own problem-solving skills. Not cool, right?

Where’s the Creativity?

Let’s be real, coding isn’t just about solving problems—it’s about finding creative solutions. AI tools are so good at giving you instant answers that they can suck the fun (and the challenge) right out of it. If we’re always letting AI take the wheel, are we even driving anymore? Food for thought.

Skill Gaps and Some Sticky Ethics

To all the junior devs out there, listen up: these tools might feel like lifesavers, but don’t let them replace your learning process. Trust me, knowing the basics will save you when the AI screws up—and it will. Oh, and let’s not forget the ethical gray areas. Who really owns the code these tools generate? That’s a rabbit hole you don’t want to fall into.

Finding the Balance

So, what’s the takeaway? Use AI tools, but don’t let them use you. Start solving problems on your own first. When you do use AI, review its suggestions like a hawk. And for the love of code, keep brushing up on your fundamentals—those are your real superpowers.

Want a deeper dive into this topic? Head over to my website: Developer Talks. Let’s keep the conversation going!

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Top comments (5)

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zethix profile image
Andrey Rusev

I've always wondered - if you are a developer who uses AI to code - how do you get promoted?

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zethix profile image
Andrey Rusev

Haha, fun thought - should employers continue doing performance interviews or should they too just ask the bot? - what are 10 things developer X did not bother to do this year? :)

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sayyad_arsalan_97175d7177 profile image
Sayyad Arsalan

AI is but a tool-Stack Overflow on steroids. The key skill lies in understanding, debugging, and fine-tuning whatever is generated by AI. If one ends up copy-pasting code, learning is neglected, thus making it challenging for one to get promoted. But, if you are the kind who uses AI to fast-track your learning and make better decisions, that’s what makes a standout developer

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zethix profile image
Andrey Rusev

Well, this is where I struggle - if you generate code, then this bit:

understanding, debugging, and fine-tuning whatever is generated by AI...

will take you longer - meaning - you'll be less productive. In contrast - while you're writing the code yourself - you're already also doing understanding and fine-tuning and even some debugging. You might not realize it, but your brain is doing it...

Or simply put - I struggle to see where's the productivity gain in AI coding. Only way that comes to mind is if you blindly trust AI code and not spend any time on understanding, debugging and fine-tuning...

And that makes me wonder - why promote someone who is either less productive or just doesn't care?

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sayyad_arsalan_97175d7177 profile image
Sayyad Arsalan

I agree with your opinion, but I think the main variable is the way AI is put into use. In case someone simply copy-pastes what was produced by AI, then no, this bear no significance on productivity; rather, it shifts the workload. The strategic use of AI helps in the following ways:

  • It automates repetitive tasks (such as the writing of boilerplate code, basic CRUD ops).
  • These assist in speeding up research and the ideation phase (instead of digging through docs or StackOverflow for timeless hours).
  • It provides alternatives that might not have been envisaged any other way.

Writing code yourself might deepen understanding, but AI acts as a force multiplier and not a process replacement: it supports and scales thinking. The best developers are those that can recognize when they can trust the AI, when to check, and when to toss bad suggestions out the window. It is less about skipping out on anything and more about accelerating data-driven decision

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