A few years ago, the idea that AI would have a meaningful role in writing actual production code felt pretty distant. Useful for autocomplete, maybe. A novelty. Now it’s a regular part of how a lot of development teams operate, and the shift happened faster than most people in the industry anticipated.
It’s worth thinking about what’s actually changing. Not just the tools, but the whole process.
The Code Writing Part Is Only One Piece of It
When people talk about AI-assisted software development, they tend to focus on code generation. And yeah, that’s real. AI can write functions, suggest implementations, and catch syntax issues before you even run anything. But the change goes a bit deeper than that.
The thing is, a lot of developer time historically went toward things that weren’t really creative work. Boilerplate. Repetitive logic. Looking up how to do something you’ve done before but can’t quite remember. AI handles a lot of that now, which means the time left over is more likely to go toward decisions that actually require judgment. Architecture choices. Trade-offs. Understanding what the user actually needs.
Honestly, that’s probably a good thing. Most developers didn’t get into this field because they enjoy writing the same kind of code over and over.
It’s Changing Who Can Build Software
This might be the bigger story. AI-assisted software development has lowered the barrier to building working applications in a way that’s hard to overstate. Someone with a solid understanding of logic and systems, but limited formal coding experience, can now produce functional software that would have been out of reach two or three years ago.
That creates some interesting questions. Does this mean the role of the traditional developer shrinks? Probably not in the way people fear. It’s more like the definition of the role is shifting. The ability to evaluate AI-generated code, spot where it’s wrong, understand why it’s wrong, and fix it intelligently is still a deeply technical skill. Maybe more important than before.
You’ll notice that teams who get the most out of AI tooling tend to be ones with strong developers already. The tools multiply existing skill. They don’t replace the need for it.
Testing and Debugging Are Getting Weird
In a good way, mostly. AI is actually pretty decent at generating test cases, especially for edge cases that a human might not think to write. It can scan code and flag things that look suspicious before they become bugs in production.
But there’s a catch. When AI writes both the code and the tests, you have to be careful about circular reasoning. The tests might pass because they’re testing what the AI wrote rather than what the software is supposed to do. That’s a real thing that happens. It requires a kind of meta-awareness from the developer about what’s actually being validated.
The Documentation Problem Might Finally Have a Solution
Nobody likes writing documentation. This is almost universal. AI turns out to be reasonably good at generating it, especially inline documentation and basic readme content. It’s not always perfect, and in some cases it’s a little generic, but it’s a lot better than nothing, which is what most codebases had before.
The more interesting use is the other direction. AI that can read an unfamiliar codebase and give you a reasonable explanation of what it does. That’s genuinely useful for onboarding, for legacy code, for any situation where you’re walking into something someone else built.
Speed Is Up, But So Is the Surface Area for New Problems
Shipping faster is real. Teams are moving faster. That’s good. But faster shipping also means more code in production, which means more potential failure points. The quality controls that used to slow things down also caught things.
The teams handling this well are the ones who didn’t just add AI to their workflow and call it done. They thought about where the new risks show up and adjusted their review processes accordingly. A little paranoia goes a long way.
The honest summary of where things are right now is that AI has genuinely changed software development, but it hasn’t simplified it as much as the demos suggest. It’s a powerful addition to the process. The process still requires people who know what they’re doing.