The Ghost in the Machine

A few months ago, I helped a friend with their personal app. It was a full stack application. It had many microservices. But the code was messy. It was stupidly coupled. Everything was connected to everything else. I thought, "This is easy. AI can fix this. AI can untangle this mess." I was wrong.

I asked the AI to refactor the code. It tried. But it kept making things up. It hallucinated. It tried to call endpoints that did not exist. It linked services that were not there. I spent more time fixing its lies than writing the code myself. I felt angry. I felt like the tool was broken.

Then I remembered the past. It felt like the old days. Back then, I would read documentation that was five years old. I would read a Stack Overflow post from 2012. I would copy the code. I would run it. It would fail. The API was different. The SDK was gone. I had to dig deep to find the truth.

Now, AI feels the same. It reads the old docs. It reads the old posts. It gives me code that looks right but is wrong. It is not broken. It is just using old knowledge. I must guide it. I must correct it. I must explain the context properly. If I do not, it fails.

But there are good parts too. I found a pocket where AI shines. Documentation. If my code is clean and makes sense, AI writes beautiful documentation. It is fast. It is accurate. It saves me hours. But I have a rule. I never let AI touch security. I never let AI touch the infrastructure. Those parts are too dangerous. If AI makes a mistake there, the whole system is compromise.

People talk about 10x productivity. They say AI makes you ten times faster. I think this is wrong. They measure lines of code. They think more code means more work done. But that is not true.

Real productivity is not about lines. It is about solving problems. It is about shipping features that work.

Do not feel crazy if AI fails you. Do not feel crazy if it works for your neighbor but not for you. The experience is uneven. It depends on the task. It depends on your skill. It depends on the model you use.

If you use a cheap model, you get bad results. If you use the top model, you get better results. If you pick the right task, like documentation, you win. If you pick the wrong task, like fixing a coupled mess, you lose.

I have been an engineer for fifteen years. I know that tools change. But the truth stays the same. You must understand the tool. You must guide it. You must not trust it blindly.

Nothing's crazy here. We're just dealing with new tools. We're all learning as we go. Just keep building, double-check the code, and ignore the line count.