Back in 2023, a lot of powerful tech leaders claimed that 80–90% of software developers would be replaced by AI in 2025. Big tech giants slowed their hiring. Now, in 2026, the picture looks very different, and the AI bills have arrived. Here’s what actually happened, with the real stories behind it.
The 2023 narrative
It started when large language models like GPT and tools from OpenAI exploded in popularity. Suddenly, building basic components and writing simple functions could be done with a quick prompt. Leaders at companies like Meta, Microsoft and Amazon talked openly about slowing down hiring because AI was supposedly going to do the work instead.
The real turning point in the hype was the marketing around Devin AI. It was positioned as the world’s first fully autonomous AI software engineer a tool that could build software end to end with no human in the loop.
But when developers tested it on independent, real-world codebases, it failed and it failed badly. It couldn’t handle long context. It couldn’t learn from its own mistakes. The same kinds of problems kept appearing again and again. What had been marketed as a finished product simply didn’t hold up.
This wasn’t just one person’s experience. An MIT study looking at roughly 300 AI deployments found that around 95% of AI pilots delivered no meaningful financial return not even a fraction of what was promised.
Why the code falls short
So why doesn’t clean, reliable code come out the other end? There are a few core problems worth understanding.
The “churn” problem. When you ask an AI to write code, it often introduces issues that only show up in local testing. So you send it back to fix the problem and instead of solving the root cause properly, it patches things with the minimum number of changes. You end up going in circles: fix one thing, break another. I’ve seen this directly when generating code and reviewing PRs the more rounds of fixes you ask for, the more tokens you burn, and the further you sometimes drift from a clean solution.
Security vulnerabilities. Studies have found that around 45% of AI-generated code contains security vulnerabilities. That’s not a small footnote it’s a serious risk for anything going into production.
Hallucination. AI will sometimes confidently tell you to use a library or function that doesn’t actually exist. It gives a wrong answer with complete certainty. This is called hallucination, and it’s one of the biggest reasons AI still isn’t as dependable as the hype suggests. If you go back and ask it what was wrong, it’ll often repeat the same mistakes — because it lacks genuine logical reasoning.
This is exactly why replacing developers outright is so difficult. Writing code is only part of the job. Guiding it, securing it, making sure there are no vulnerabilities in the application AI can’t reliably do that on its own. At best it can assist software engineering, not replace it.
The second gap: infrastructure cost
Everything you do with these tools gets converted into tokens, and tokens become a bill. The more code you generate and the more you depend on AI for large chunks of a production system, the faster those costs climb and they can run into enormous numbers.
It also creates a hidden dependency. When a large portion of your production code is AI-generated, you still need humans who understand it well enough to debug it when something breaks. And to debug AI-written code, you first have to understand what it was trying to do.
That’s where the real time goes not in creation, but in correction. Debugging AI output is often the most time-consuming part of the whole process. Which is exactly why strong fundamentals matter so much. If your concepts are solid and you can debug these systems, your job is safe.
The junior developer myth
One claim I strongly disagree with is the idea that entry-level roles and internships are disappearing because AI now handles all the basic work, so companies supposedly don’t need to hire for junior positions anymore.
This is completely wrong, and it can’t work long-term. If we stop hiring juniors today, where do the seniors of tomorrow come from? Senior expertise is built over years depth of knowledge, depth of experience, time spent inside real systems. That knowledge has to be passed down inside an organization, and that only happens when you bring people in at the junior level and let them grow.
So, no software developers are going anywhere.
