He still remembers the old days, when the screen was just a blank canvas, and every line of code was a conscious choice. Back then, a new project started with a blank file and the slow, deliberate work of building something from nothing. He would spend hours meticulously crafting the perfect snippet, hunting for a missing semicolon, or stepping through a debugger line by painful line. It was a craft, a puzzle that demanded full attention and every ounce of his skill.
Then the AIs came.
The first was a small, almost unnoticed addition to his text editor—an autocomplete feature that did more than just finish his variable names. It started to guess entire lines, then whole blocks of code. He found himself sailing through routine tasks, the boilerplate melting away as the AI offered up perfect, pre-written sections. Debugging became a game of spotting the flashing red hints on the side of his screen, pointing to a bug before he even ran the program. The AI could even comb through his code and find subtle security holes he might have missed. He started to feel a strange sensation—not of being a craftsman, but of being an architect, sketching out a plan while a tireless assistant handled the tedious construction. His work was faster, cleaner, and more creative than it had ever been. He could finally focus on the truly interesting problems, the kind that made his brain hum with possibilities.
But he also noticed a change. He started to forget the fundamental commands. The simple muscle memory of writing a function from scratch began to fade. When his internet connection was slow or the AI tool glitched, he felt a moment of panic—a brief, unsettling realization that he wasn’t as sharp as he used to be. He heard whispers in the industry, too, about a new wave of developers who seemed lost without their digital crutches. People talked about entry-level jobs disappearing, the routine work that once taught new coders the basics now being handled by an algorithm.
Sometimes, the AI’s suggestions were just… off. They would pull in code that looked fine on the surface but was built on a flawed logic or, worse, a subtle bias from a vast, messy dataset. He had to be more vigilant than ever, not just checking for bugs but for the kinds of hidden flaws a machine could introduce. He also worried about the other side, about the new, sophisticated malware that was popping up, clearly created with the same kind of AI assistance he was using. It felt like an arms race. The new tools were powerful, but they also required new skills—learning to prompt them, to vet their output, and to integrate them into his existing flow. It wasn’t as simple as just flipping a switch.
Now, as he looks at his code, he knows it’s a collaboration. A partnership between his human intuition and the machine’s speed. The AI is a powerful assistant, but he knows he can never forget his own part of the bargain—to think, to question, and to truly understand what the code is doing beneath the surface. He is still the one in charge. For now.