Aditya Agarwal Posted on May 30 AI coding assistants are creating mass dependency and we're pretending it's productivity # ai # productivity # career # githubcopilot Using Copilot feels a lot like driving a Tesla. It might be quietly eroding your ability to code without it. A couple of months ago, I realized something that made me feel uneasy. I was on a plane, disconnected from the internet, and I had to write a utility function. I mean, from scratch. It wasn’t anything special. Just a debounce. I sat in my seat, and I looked at my text editor as if I had completely forgotten how to write. I was so scared. The Autocomplete Crutch Is Real With more than 1.8 million paying subscribers, GitHub Copilot is not just a niche tool. It's become the industry standard. And the thing is — I'm guilty of that. I mean, I use it every day. I'm not saying we should all turn into luddites. But I've started noticing a pattern on my team and in myself. Our ability to write code from intent has been diminishing. We are no longer problem-solving. We are just tab-completing. Faster ≠ Better Here's what I think people confuse: → Speed of output is not the same as depth of understanding → Accepting a suggestion is not the same as solving a problem → Shipping a feature is not the same as knowing why it works When Copilot gives you a piece of code and you press Tab, you miss the section where your brain creates a mental image. That's the part that makes you a better engineer over time. That's the part that lets you debug at 2 AM when the AI-generated code breaks in production and the suggestion engine has no context for your specific mess. Educators are already sounding the alarm. Students are submitting AI-generated code they can't debug when it fails. They never built the understanding because they never had to struggle through it. 🧠 The Dependency Nobody Talks About We discuss dependency management in software a lot. We do npm audit. We fret abo
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AI coding assistants are creating mass dependency and we're pretending it's productivity
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