What’s the deal with AI replacing software engineers?

A senior software engineer I spoke to recently said this to me:

“AI is a skill multiplier. But it’s also a Dunning–Kruger effect multiplier.”

What he meant was that, if you already know what you’re doing, AI can make you better. But if you don’t, it can give you the illusion that you’re better than you actually are, which is a problem.

Just to be clear, I’m not a developer. I’m a marketer who works closely with engineers and occasionally vibe-codes small tools for work and fun. So I’ve seen AI feel magical. I’ve also seen it produce code that looks perfect… until you try to change a few small things and the whole tool falls apart.

So I don’t think the big question is “Will AI replace developers?”

A better question is: what happens to the way we grow developers?

AI helps the people who already know what they’re doing.

The engineer I told you about has been coding for 15 years. He fully integrated AI into his daily workflow.

He told me that since doing that, he’s outperforming his team by a mile. Not just because AI made him faster. But because he already knew what “good” looked like.

He said he can spot bad output instantly. He also said he can fix broken logic quickly. Which means he can guide the tool instead of him being guided by it.

So, the more skill you bring, the more AI seems to amplify it, which means the gap between experienced engineers and everyone else might actually grow with AI usage.

And that’s bad. It means fundamentals and skill matter more than ever.

Vibe coding feels great until things get complicated.

I interviewed another engineer who ran a little experiment. He decided to build a simple game in an area he had no experience in. For that he intentionally relied on AI heavily—minimal context and lots of prompting.

He said it worked at first. He got a basic version running quickly.

Then he said the logic got slightly more complex, which he said was awful.

The AI-generated code had bugs everywhere. Messy code and no real understanding of what had been generated.

But he said something that really stood out: the last 1% of the project took as long as the first 99%.

That last 1% was debugging. And debugging is where real experience shows.

He said you can generate code with prompts. But you can’t prompt your way out of deep confusion if you don’t understand what’s really happening.

Junior developers still need the messy middle.

Right now companies are slowing junior hiring while they figure out what AI can and can’t do. That makes sense from a business perspective. When new tools appear, you’d want to test them.

But the thing here is every senior developer right now was once a junior who didn’t know what they were doing.

They built their experience by writing buggy code, by breaking things, by debugging during late nights, and by asking questions.

So, if AI handles all those beginner-level tasks, where would juniors get their experience from?

The second engineer I interviewed said he’s a better programmer mostly because he’s good at debugging. That skill didn’t come from shortcuts, but it came from years of fixing broken code.

You don’t develop that instinct by watching code appear on a screen. You develop it by getting stuck and working through it.

AI replaces simple work before it replaces deep work.

The first engineer I spoke to compared this moment to building websites using WordPress. When WordPress first became mainstream, it made it easier to build simple websites like this easier. But it didn’t eliminate web development. It’s just that the easy stuff got easier.

Right now, AI feels similar. It handles boilerplate code. It speeds up repetitive tasks. And it helps draft documentation, which is all a huge timesaver.

But what about building something that still works when a million people use it?
What about deciding what to sacrifice so it runs faster or stays secure?
And what about fixing the strange little bugs no one saw coming?

Those all still need an experienced engineer.

The real skill is how we choose to use the tool.

I don’t think this is a battle between “AI believers” and “AI skeptics.” Most serious engineers I know use AI every day. But they just use it carefully.

I think here’s what it really comes down to.

Some people use AI to move faster, while others use it so they don’t have to think as hard, which is where the skill gap shows up.

The first group gets better over time, while the second group kind of stays where they are or may even regress due to over-reliance on AI.

And the AI tool doesn’t decide which direction you go. You do that.

Here’s where I landed.

AI will expose weak foundations and fundamentals. But it will not wipe out software development. It will raise expectations, and it will also speed up skilled engineers.

If we continue hiring juniors and training them properly, AI gives them a boost and in turn to the entire software industry.

But if we stop doing that, we will see really bad code in the future.

From where I sit—as a marketer who builds small tools and spends a lot of time around engineers—the future doesn’t look like “AI replaces engineers.” Rather, it looks like this: Engineers who understand what the AI writes will thrive. But engineers who don’t will struggle.

And the ones who thrive won’t just be great at prompting. They’ll be great at knowing when the prompt and the output are wrong.