This is pretty interesting.

The other day, I came across a guy on How-To Geek sharing his experience with “Vibe Coding” using GitHub Copilot. Honestly, I couldn’t help but laugh—it was the exact same pitfall our team stumbled into last year.

So-called “Vibe Coding” is essentially going with the flow and letting AI write code for you. Sounds cool, right? But in practice, it’s nothing like what you’d imagine.

First, prompts aren’t as simple as you think.
The guy assumed he could just throw in a few random words and the AI would understand. Instead, he got a screen full of garbage code. I related way too hard—last year, one of our interns typed “make a login page” into Copilot, and the generated code stored passwords in plain text. We’ve since dubbed this “wishful programming”: you think you’re writing requirements, but you’re really just making wishes to the AI.

Second, the code quality is way worse than expected.
AI-generated code might run, but upon closer inspection, it’s often a mess of “legacy code” vibes. In that case study, Copilot autocompleted a recursive function but forgot to include a proper termination condition. The kicker? This kind of code easily passes basic tests, only to cause problems deep in the system later. Now, we have a rule: AI-written code must be manually reviewed line by line—more exhausting than deciphering someone else’s spaghetti code.

Third, the line between human and machine collaboration gets blurry.
The article mentioned a telling detail: developers unconsciously start following the AI’s lead. For example, if the AI suggests using some obscure library, you’ll actually go read its docs. That’s terrifying—you think you’re controlling the AI, but really, it’s leading you astray. We now enforce a strict policy: every AI suggestion must survive the existential question, “Why the hell is this thing smarter than me?”

Honestly, what struck me most about the How-To Geek case wasn’t the technical details but how the industry’s mindset has shifted. Six months ago, everyone was debating “Will AI replace programmers?” Now, the question is “How do we stop AI from making programmers collectively crash and burn?”

Here’s something ironic: seasoned Copilot users actually code slower than beginners. Why? Because they spend time crafting prompts, double-checking outputs, and reverse-engineering the AI’s “black-box logic.” This completely flips the common belief that “tools should boost efficiency.” It’s more like learning to drive—your instructor keeps yelling, “Don’t rely too much on the backup camera!”

At this point, AI coding tools aren’t some “magic auto-code generators.” They’re more like a co-pilot who’s sometimes a genius and other times utterly clueless. As our CTO puts it: “They can upgrade development speed from a bicycle to a motorcycle—but if you let go of the handlebars, you’ll hit a tree in seconds.”

(Just realized I’ve gone over the word count again
 Next time, I’ll share our team’s AI Coding Survival Guide, which has practically become our bible.)