AI will replace programmers. Here's how, and why.

As a programmer, I understand the fear of being replaced by AI. It’s comforting to see others on social media confidently claim that AI isn’t smart enough to take our jobs. Their optimism makes it easy to believe we’re safe—at least for now.

But they’re wrong. AI can and will replace programmers.

In this post, I’ll break down the common arguments made by AI skeptics who insist their jobs are irreplaceable. By the end, you’ll see why programming, as we know it, is on the path to automation.

Argument #1: AI hallucination produces incorrect code

AI often generates nonsense due to hallucination. This happens when a language model can’t find a direct answer in its context, so it fabricates one instead. Because of this, some argue that AI can’t produce reliable code.

But what about human programmers who write incorrect code just to appease their managers? Does that mean they hallucinate too? Absolutely—they do it all the time.

In the corporate world, programmers are often afraid to admit they don’t know something. So, when asked to implement a feature, they hallucinate—they guess, ship the wrong feature, and wait for QA to point out the mistakes. This cycle repeats until the correct implementation emerges.

AI can follow the same process. When it produces incorrect code, you provide feedback until it gets it right.

Argument #2: AI can’t use clever code

AI might sometimes use an outdated algorithm or design pattern when generating code. So, does that mean you always need a human programmer to ensure optimal performance?

Not necessarily. After all, programmers make the same mistakes all the time. They might use an array instead of a hash table for keyed lookups, apply an unnecessarily complex design pattern, or skip patterns entirely, leading to excessive code duplication.

Even if an AI selects the wrong algorithm or design pattern, you can simply use the feedback loop mentioned earlier to guide it toward a more efficient or cleaner solution.

Argument #3: AI isn’t creative

AI is trained on specific data, and unlike humans, it doesn’t “learn” in the traditional sense. Because of this, some argue that AI can’t design new algorithms or come up with truly innovative solutions.

But that’s not true. We’ve already established that AI’s hallucination allows it to generate code with arbitrary creativity.

Besides, programming is largely a solved problem where true innovation is rare. Just look at the programming ecosystem in 2025—developers are constantly creating new web frameworks and build tools, but each one solves some problems while introducing new ones. It’s a never-ending cycle, like a hamster wheel. The reality is that most programmers aren’t innovating; they’re just building and consuming frameworks.

Take my own work as a Django developer. I import libraries, plug in my own logic, and I’m done. There’s nothing in that process that AI can’t replicate.

Innovation itself is just trial and error. And thanks to AI’s hallucination, we can leverage the feedback loop to refine its output until it produces something genuinely innovative.

How AI will replace programmers

As you can see, programmers aren’t inherently better than AI at building software—after all, AI is modeled after human thinking.

We, as programmers, also hallucinate, write inefficient code, and rarely innovate. So why not let AI handle our jobs while we focus on work that truly requires human creativity?

AI is still in its early stages, but as models improve and get trained on more software development patterns and new code, they will inevitably replace programmers.

Instead of hiring programmers, companies should invest in QA teams to review AI-generated code—just like they’ve always done with human developers.

Conclusion

When machine code replaced punch cards, programmers had to adapt to keep their jobs. Then came assembly language, making programming more accessible to those who couldn’t work directly with machine code.

Higher-level languages like C pushed accessibility further, followed by Java and Python—allowing people without a computer science background to write software.

Each step made programming easier, but none of these innovations aimed to replace programmers entirely. Even with Python, machine code specialists were still needed for high-performance tasks.

AI, however, is different. It’s not just making programming easier—it’s making it unnecessary for many.

Instead of hiring a developer to build an app, a non-technical founder can use AI to generate code, test it, and pass it to a QA team (which could include former programmers) to verify correctness. The programmer is removed from the equation.

It’s understandable that programmers feel threatened, but this evolution is necessary. Programming is a solved problem. To prevent stagnation, humans need to tackle new challenges.

As a programmer, I welcome this shift—and I’m excited to see what I’ll be doing next.