I’m done writing code.
In fact, I may not even be a “programmer” anymore—at least not in the traditional sense.
The software development landscape is shifting beneath our feet. Tools are evolving faster than companies can adopt them, and the line between writing code and orchestrating intelligent systems is blurring. These are exciting times, but also strange ones.
Just the other day, I overheard a coworker proudly claim that AI wrote 99–99.9% of the code for their latest application. It wasn’t a confession—it was a flex. They were bragging. And while it struck me as a little odd at first, I realized they weren’t wrong to be proud. The future is here, and it’s writing code.
The Rise of Conversational Development
We now live in a world where you can hold a conversation with artificial intelligence and, as a result, deploy a fully functional application. Not a prototype. Not a demo. A live, working product.
These tools are improving weekly. Just two years ago, what we’re seeing today felt like science fiction. Now, junior engineers are anxious about their job security, while others—with barely a grasp of development—are overconfident in their ability to build complex systems with a few prompts.
Entire disciplines are being condensed into a single chat window. It’s strange. But it’s also powerful.
The Dream (and Danger) of AI-Powered Engineering
I’ll admit it: using these tools is a dream. I’m accomplishing more in less time and solving problems faster than ever before. My workflow has never felt smoother.
Right now, I’m hooked on Cursor, though I still bounce between VS Code with GitHub Copilot, OpenAI’s tools, and TypingMind. I’ve tested Pythagora, glanced at Windsurf, and explored a variety of other platforms. I want to try everything, because each week brings something new—and often impressive.
But amid all this excitement, I feel the need to raise a flag of caution—especially for new engineers, or those who suddenly feel like they can do anything with AI.
Here’s the thing: if you can’t read the code being generated, you’re heading into dangerous territory. It’s easy—almost too easy—to generate massive amounts of code without understanding what it does. This leads to bloat, complexity, duplication, and eventually, confusion. When something breaks, you might not even know where to look.
That’s when the real problems begin.
Don’t Let Your Brain Atrophy
There’s also a subtler issue: brain rot.
Over-reliance on AI tools can stunt your growth. Just because you can build something quickly doesn’t mean you understand how it works—or how to fix it when things go wrong.
A recent example from my own experience: my boss reached out in a panic about an “exposed API endpoint.” It sounded serious—until I realized the endpoint was simply serving public content for a blog. The data wasn’t sensitive, and the endpoint was meant to be public. But the terminology spooked people who didn’t understand what they were seeing.
This is a reminder: know your craft. Fill in the knowledge gaps. Being able to explain what’s happening, what’s at risk, and why it matters is part of being a great engineer. Don’t let the tools do all the thinking for you.
From Writing Code to Curating It
These days, I write a lot less code by hand. Instead, I’m cherry-picking from AI-generated snippets. What started as a way to brainstorm solutions has evolved into a pipeline for production-ready code. And it’s good code. Often better than what I would’ve written myself.
It’s saving me time. It’s making my job easier. And I love it.
But I also worry—about my relevance, about how my skills need to evolve, and about what it means to be a developer in a world where machines do most of the coding.
Soon, anyone might be able to “code” simply by describing what they want. Imagine passing an AI a URL and saying, “Build me a Google Maps-style component for the Locations page on my website,” and watching it deliver the perfect result in under a minute.
That future isn’t far off.
Maybe We Need a New Title
Perhaps it’s time we retire the term “coder.” Because in the not-so-distant future, very few people will write raw code anymore.
Instead, we’ll be curators, architects, communicators—people who understand problems deeply and work with AI to solve them. We’ll still need to know the fundamentals. We’ll still need to understand the systems. But the nature of our work is evolving.
And honestly? I’m here for it.
Just don’t forget to keep learning. Because while the tools may change, the need for critical thinking, problem-solving, and understanding will always remain.