We've all been there. You open a new terminal, run a quick prompt in your IDE, and boom—fifty lines of perfectly indented, seemingly functional code appear out of nowhere. It feels like magic.
Whether you are using Copilot, ChatGPT, or any other LLM, there is no denying that AI code generation has completely shifted how we build software. We aren’t writing boilerplate anymore; we are orchestrating it.
But this massive jump in velocity has brought an unexpected side effect: we are stopping to read the code less and less. Because the AI gives us something that looks right, the temptation is to hit Tab, run the dev server, see if it compiles, and move on. But if you’re a junior developer—or honestly, any developer trying to stay sharp—skipping the reading phase is a massive trap.
Let's talk about the pros, the cons, and why reading the actual code that the AI produces is probably the most critical skill you can develop right now.
The Good, the Bad, and the Autocomplete
Don’t get me wrong; I’m not a purist who thinks we should go back to writing everything from scratch in a plain text editor. AI is an incredible tool if used correctly.
The Pros
- Context switching is cheaper: If you forget the exact syntax for a complex CSS grid layout or a specific array method configuration in JavaScript, the AI saves you a trip to MDN or Stack Overflow.
- Boilerplate is gone: Setting up standard configurations, basic API fetch wrappers, or repetitive mock data takes seconds instead of twenty minutes.
- Instant rubber-ducking: You have a conversational partner right inside your editor to help you break down complex logic.
The Cons
- The "It Works, Don't Touch It" Syndrome: This is the biggest danger. When code is generated instantly, you lose the cognitive attachment to it. If it works, you ship it—even if you have absolutely no idea why it works.
- Hallucinations and Technical Debt: AI models are guessing the next best token based on patterns. They don't think structurally about your specific app architecture. They will happily invent deprecated lifecycle methods, introduce silent security flaws, or write terribly unoptimized loops that will bite you in production three months from now.
The Main Event: Reading is the New Writing
When we used to write code line by line, the act of typing forced us to process the logic. We had to think about variable scoping, edge cases, and imports because our fingers were physically committing those lines to the file.
Now that the writing part is mostly handled for us, our primary job description has shifted. We are no longer just code writers; we are code reviewers.
If an AI generates a block of code for you, your immediate next step shouldn't be to test it in the browser. It should be to read it. Line by line.
Cognitive overload is a real thing, especially when you're staring at a screen full of logic you didn't actually write. It's incredibly tempting to just skim the surface to save some mental energy.
But if you don't read the code before you accept it, you are essentially importing a random third-party package written by a stranger into your codebase without checking its source. You wouldn't do that with an npm package, so don't do it with an LLM prompt. Take a breath and slow down enough to actually digest what's on the screen.
Ask yourself:
- Why did it choose this specific array method over another?
- Are there any hidden side effects in this async function?
- What happens if the API returns an error or an empty array? Is that case handled?
Treat the AI like a Senior Developer (But a Lazy One)
If you are a junior developer, you might feel like you're missing out on traditional mentorship if your team is busy or fully remote. Here’s a better way to look at it: Treat the AI as your senior developer and mentor.
When a senior developer hands you a code snippet or explains a concept during a code review, you don't just blindly nod and paste it in. You try to understand it. And if you don't, you ask questions.
You can do the exact same thing with an LLM. If it generates a complex function utilizing closures or a design pattern you haven't seen before, don't just hit accept. Use it as a teaching moment.
Highlight the block and prompt it:
"Explain this specific block to me line by line. Why did you choose this approach instead of a simple loop?"
By doing this, the AI stops being a shortcut and starts being a hyper-personalized textbook. But remember—just like a real human senior developer, the AI can be wrong, tired, or lazy. It will occasionally give you a solution that is over-engineered or outdated just because it saw it a thousand times in its training data. It’s up to you to keep it honest.
Conclusion
At the end of the day, speed is great, but comprehension is what builds a career. The developers who thrive in this new era won't be the ones who can generate code the fastest; they will be the ones who can read, debug, and architect it the best.
Next time you trigger a code generation shortcut, take a breath. Step away from the mouse, read through the lines on your screen, and make sure you own the code—don't let the code own you.
What’s your approach to using AI in your day-to-day workflow? Do you find yourself reading less code or more? Let me know your thoughts over on @stefanbc.
