Coding with AI: Shortcut to Success or Road to Mediocrity?

As someone who is very keen about keeping up with the trends, I found myself scrolling through a Reddit thread with Sr Developers and Jr Developers engaged in a passionate debate about the use of AI and its potential and shortcomings.

It got me thinking, Artificial Intelligence (AI) is shaking up the coding world, churning out lines of code faster than you can say "Hello, World!". For seasoned developers, it’s like having a superpower that's zapping away tedious tasks so they can focus on the fun, creative stuff. But for someone just starting out—maybe a student, a junior dev, or even an intern AI might feel like a cheat code that’s too tempting to resist.

The big question I set out to answer is does AI help you grow, or does it quietly sabotage your learning?

The Magic Wand of AI in Coding

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Today, AI is able to write code much faster than any developer. This is a gamechanger.

Need some boilerplate stuff?

AI’s got it covered in seconds.

Want to test a wild idea?

AI spits out a working draft before your coffee’s cold.

Spotting errors and suggesting fixes?

AI's got it done, no sweat.

Even non-coders are getting in on the action with AI-powered no-code tools, building apps without ever touching a keyboard.

Honestly, it’s democratizing tech in a way that has been unimaginable for the longest time.

But if your goal is to become a coder—not just dabble and test—there’s a catch.

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The Sneaky Trap for Newbies

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For beginners, AI is like a calculator in math class. Sure, it gives you the answer quickly and can help you boost productivity, tackling the most difficult problems quickly, but if you use it as a crutch, you never learn how to solve the problem yourself, no matter how basic. Similar is the case for Code and AI.

Here’s where copying AI gets dicey:

• Surface-level smarts: Copy-pasting AI’s code mindlessly feels great until you realize you don’t know why it works. That’s a shaky foundation waiting to crumble.
• Missing the grind: Debugging is an integral part of coding. It's not uncommon to receive poorly written code. Being able to debug it manually helps one understand the functionality of the code overall, thereby helping you become a better coder overall.
• AI as a lifeline: Get too cozy with AI, and you’re lost when it’s not around—like during a whiteboard interview or a system crash.

It’s not all negatives, though. The trick is knowing when to lean on AI and when to go solo.

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A Real-World Example: Two Coders, Two Paths

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Let’s use the example Gaurav and Divya, two fresh-faced junior devs tasked with building a REST API. They both have the same gig, however, their approaches vary significantly.

Gaurav is the AI Shortcut King He is all about speed. He fires up an AI tool, punches in a prompt, and boom—working API in minutes. “This is the future,” he thinks, grinning. “Why slog through manuals when I’ve got this?”

It’s smooth sailing until…. the API tanks under a server overload. Gaurav stares at the error logs, heart racing, totally lost. He didn’t write the code—he just borrowed it. Now he’s stuck, Googling in a panic while his boss taps her foot.

Divya’s different. She is the Slow-and-Steady Star.

She cracks open docs, scribbles code, and hits roadblocks—lots of them. “Why won’t this stupid endpoint work?” she mutters, debugging for hours.

It’s messy, but every fix teaches her something: how HTTP methods play nice, why error handling matters. When that server overload hits, she’s ready. “Database needs optimizing,” she says, fixing it like a boss. Her hands-on hustle pays off big time.

Same problem, two outcomes.

Gaurav got a quick win but didn't learn anything as AI did all the work for him.

Divya on the other hand built skills that stick; however, the process was tedious and long.

That’s the AI dilemma in a nutshell.

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How to Use AI Without Losing Your Edge

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In my experience, the solution to this dilemma actually lies somewhere in the middle. You don’t have to ditch AI to become a solid coder. But you also can’t only rely on it. It’s all about balance. Here’s how to make it work for you:

• Use it as a guide: Stuck on a loop? Use AI understand the logic or suggest a step, not write the whole thing.
• Try it yourself first: Solve a problem yourself, then run it by AI to spot gaps or to get it polished. It’s almost like a teacher grading your homework after you’ve tried.
• Blend old and new: Mix AI with classic learning—think coding puzzles, pair programming, or a mentor who’s seen it all. Variety keeps you sharp.

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Skills AI Can’t Touch (Yet)

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As we move into a reality where AI is faster and more efficient, it's important to figure out how one can differentiate oneself. The trick is to actually use AI in smarter ways and stand out from the rest but focussing on what it can’t fake:

• Nail the fundamentals: Data structures, algorithms, logic—these are your coding DNA. No shortcuts here.
• Get weird with it: Build something quirky—a retro game, a niche app. Creativity is your human superpower.
• Join the crew: Hit up forums, hackathons, or open-source projects. Real-world projects and insights beat AI’s bubble every time.
• Own your flops: When code breaks, don’t just fix it—figure out why. That’s how you grow guts and brains.

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Wrapping It Up: You’ve Got This

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The reality of today is that AI’s here to stay, and it’s awesome provided you use it right. For beginners, it’s a tool, not a teacher.

Lean into the hard stuff. That is what will actually help you grow. You can use AI as a wingman, but keep building skills that last. You’ll end up a coder who can roll with anything, AI or no AI.

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