How Coding with AI Can Mess with Your Confidence (and Why That’s Okay)
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Let me tell you about my buddy Alex. He’s one of those people who makes coding look easy—like, "Oh yeah, I’ll just whip up an algorithm that does this impossible thing in five minutes" kind of guy. But recently, something changed for him.
Instead of relying solely on his own brainpower, he started leaning into tools like GitHub Copilot , Cursor IDE , and even some custom-tuned large language models (LLMs) designed specifically for coding.
The result? A tool so slick, so smart, and so useful that it could save developers hours every week.
But here’s the kicker: Alex hated showing it to anyone.
When I asked why he hadn’t shared it yet, his response hit me like a ton of bricks:
“It doesn’t feel real,” he said, staring at his screen like it might judge him. “I didn’t write all of it myself. How can I call it mine?”
And honestly, that moment broke my heart because it reminded me how much imposter syndrome sneaks into our lives—even when we’re doing incredible work. It’s not just Alex either; I’ve seen this happen to tons of devs who start using AI-powered tools.
So let’s talk about what’s going on here, why it happens, and most importantly, how to deal with it.
Hazem Abbas
When AI Becomes Your Co-Pilot
Here’s the deal: Alex’s project wasn’t your average app or script. It was a productivity powerhouse—a tool that automated boring tasks, suggested optimizations, and even generated boilerplate code based on plain English prompts. Sounds amazing, right? Well, here’s the catch: most of the heavy lifting came from AI assistants like GitHub Copilot, Cursor IDE, and other LLMs fine-tuned for coding.
- GitHub Copilot suggested entire functions.
- Cursor IDE helped refactor messy sections into clean, readable code.
- Custom-tuned LLMs gave him ideas for edge cases he hadn’t thought of.
To anyone looking at the final product, it would seem like pure genius. But to Alex, it felt… hollow.
“I kept thinking,” he told me over coffee one day, “if I didn’t come up with every single detail, am I really the creator? Or am I just… a middleman?”
This mindset is where things get tricky. Using AI doesn’t erase skill—it enhances it. Think of these tools as super-smart assistants. They suggest things, sure, but they don’t make decisions for you. Without Alex guiding the process—choosing which suggestions to accept, tweaking outputs, refining logic—the tool wouldn’t exist. Yet instead of celebrating his role as the architect, Alex fixated on what he perceived as shortcomings.
Hazem Abbas
Why Does AI Trigger Imposter Syndrome?
Humans are wired to take pride in their creations. There’s something deeply satisfying about knowing you built something from scratch, brick by brick, byte by byte. But when AI enters the picture, that sense of ownership gets blurry. You start questioning whether your contribution matters enough. Are you still the master builder, or just someone following instructions?
Take Alex’s situation. Every time he looked at his tool, he saw not his own ingenuity but the fingerprints of AI. The irony? Those fingerprints only appeared because he knew how to use them effectively . Coding with AI requires more than just clicking buttons—it demands deep understanding, critical thinking, and creativity. For example:
- Problem-Solving : AI can spit out lines of code, but it can’t identify the root cause of a problem unless you guide it.
- Contextual Awareness : Tools like GitHub Copilot may suggest generic solutions, but it takes human insight to adapt them to specific scenarios.
- Refinement : Raw AI output often needs fine-tuning. Only someone with solid coding chops knows how to polish rough edges into brilliance.
In other words, AI doesn’t replace coders—it empowers them. As Albert Einstein famously said, “Imagination is more important than knowledge.” In Alex’s case, his imagination drove the project forward, while AI provided the scaffolding. But imposter syndrome made him forget that distinction.
The Double-Edged Sword of Modern Tools
The world of software development is evolving fast, thanks to dozens of open-source IDEs, services, and LLMs designed to assist developers. From Visual Studio Code extensions to platforms like Replit Ghostwriter , there’s no shortage of resources to lean on. These tools aren’t meant to replace us—they’re meant to amplify our abilities.
But here’s the catch: the easier it becomes to create incredible things, the harder it gets to believe we deserve credit for them. When everything feels automated, it’s easy to downplay our contributions. We start thinking, “Anyone could’ve done this.” Spoiler alert: They couldn’t.
Even Hollywood legends wrestle with self-doubt. Remember Tom Hanks’ quote about imposter syndrome? He said, “No matter what we've done, there comes a point where you think, ‘How did I get here? When are they going to discover that I am, in fact, a fraud and take everything away from me?’” If someone like Tom Hanks feels this way, imagine how hard it hits us mere mortals.
For Alex, the fear of being “found out” overshadowed his achievements. Instead of seeing his tool as evidence of his skills, he viewed it as proof of his limitations. That’s the cruel paradox of imposter syndrome—it convinces you that your strengths are weaknesses.
Hazem Abbas
Overcoming the Shadow of Doubt
So, how do we break free from this mental trap? Here are a few strategies that worked for Alex—and might work for you too:
- Acknowledge Your Role : Whether you’re using AI or not, remember that you are the one steering the ship. AI is a tool, not a replacement. Celebrate the fact that you know how to wield it effectively.
- Focus on Impact : Shift your perspective from “Did I do this perfectly?” to “Is this helpful?” Alex’s tool solved real problems for potential users. That impact mattered far more than how it was created.
- Embrace Collaboration : Great projects rarely happen in isolation. Just as teams collaborate to build software, consider AI another collaborator. Its contributions don’t diminish yours—they complement them.
- Talk About It : Sharing your struggles with others can be incredibly liberating. Chances are, your peers feel the same way. As Neil Gaiman put it, “The first problem of any kind of growth is recognizing that you’re not the person you were before.”
- Celebrate Small Wins : Break down your journey into milestones. Did you successfully integrate AI-generated code? Did you refine an output until it worked flawlessly? Those are wins worth celebrating.
What Developers Should Know About Using AI
If you’re new to AI-assisted coding, here’s a quick rundown of tools and tips to get you started:
Tools Worth Trying:
- GitHub Copilot : Great for generating code snippets, completing repetitive tasks, and offering suggestions in real-time.
- Cursor IDE : A next-gen editor that integrates AI seamlessly into your workflow. Perfect for refactoring and debugging.
- Tabnine : Predicts variable names, function calls, and even entire blocks of code. Super handy for speeding up mundane tasks.
- Custom-Tuned LLMs : Some companies train their own LLMs for niche use-cases. If you have access to these, they can be game-changers for domain-specific projects.
Hazem Abbas
Use-Cases to Explore:
- Boilerplate Generation : Let AI handle the boring stuff like setting up CRUD operations or configuring API endpoints.
- Debugging Assistance : Feed error messages into an LLM and watch it suggest fixes.
- Learning New Languages/Frameworks : Use AI to translate concepts between languages or frameworks you’re less familiar with.
Advice for Developers:
- Don’t Rely Blindly : Always review AI-generated code. It’s great at suggesting solutions, but it doesn’t always understand context.
- Learn to Prompt Effectively : The better your input, the better the output. Practice writing clear, concise prompts.
- Stay Curious : Experiment with different tools and see what works best for your style. There’s no one-size-fits-all solution.
Why Businesses Want AI-Savvy Developers
Here’s the truth: businesses and agencies are actively looking for developers who know how to leverage AI. Why? Because it saves time, reduces costs, and boosts efficiency. If you can show that you’re comfortable integrating AI into your workflow, you become a highly valuable asset.
Imagine walking into an interview and saying, “Yeah, I used GitHub Copilot to cut development time by 30% on my last project.” Or, “I trained a custom LLM to automate customer support ticket responses.” That’s the kind of thing hiring managers drool over.
The Bigger Picture
At the end of the day, Alex decided to publish his tool—not because he fully conquered his imposter syndrome, but because he realized holding back wouldn’t change how he felt. Sometimes, action beats perfection. And guess what? People loved it. Developers praised its efficiency, designers admired its simplicity, and clients raved about its functionality.
Looking back, Alex admitted, “I thought publishing it would make me feel like a fraud. Instead, it reminded me that I’m capable of creating value—even if I didn’t do it the ‘traditional’ way.”
That’s the beauty of working with AI: it challenges us to redefine what success looks like. It pushes us to embrace new methods without losing sight of our core skills. Most importantly, it forces us to confront the nagging voice inside our heads telling us we’re not enough—and prove it wrong.
Because here’s the truth: imposter syndrome isn’t a sign of failure—it’s a sign of growth. If you never doubted yourself, you’d never push beyond your limits. So next time you feel like a fraud, channel Alex’s story. Remember that greatness isn’t about doing everything alone—it’s about making the impossible possible, one step (or one line of code) at a time.
And hey, if AI helps along the way? That’s not cheating—it’s innovating.
Final Thought: Don’t let imposter syndrome hold you back. Lean into AI, trust your instincts, and keep building. The future belongs to those who know how to blend human creativity with machine intelligence.
