ENG | AI: The 10x Productivity Myth

Everyone is talking about 10x productivity with AI. I tried it. Here’s what actually happened.

I built a MOD file player - an obscure 90s audio format. I knew the format well, verified every step, added features incrementally. A few sessions, something that would have taken me three rainy weekends. That’s close to 10x at early stage.

Then I tried adding support for a more complex format. I didn’t know the spec well, approved too many changes at once, lost the ability to verify what was happening. The result compiled. It produced noise with hints of melody. The AI had implemented it as a hack on top of the simpler format - reusing code in a way that violated every principle of good design. It was hardly possible to fix. AI does not know if it sounds right. It can only guess errors which I’m not able to pinpoint.

Where AI genuinely helps: rapid prototyping, initial research, and rubberducking. That last one is underrated - the act of precisely describing your problem to AI often leads you to the solution yourself, before it even responds.

Where it struggles: integrating changes into existing code. It sees files. It doesn’t see why things are the way they are. It does not know past decisions which live in people’s heads. It even failed on its own code.

My honest estimate: 20-50% productivity boost. More on isolated, well-defined problems. Less when the knowledge isn’t in the code.

One more thing worth saying out loud: sharing your company’s codebase and business logic with an external AI service may not be a good idea. And regardless of how the code was written - you are responsible for it. You need to understand it and verify it. That doesn’t change.

The 10x headline is real in specific conditions. It’s not general. And knowing the difference matters more than chasing the number. I’m not saying AI is useless. But expectations and hype are not realistic.