The Economics of AI Coding: A Real-World Analysis
January 15, 2026
My whole stream in the past months has been about AI coding. From skeptical engineers who say it creates unmaintainable code, to enthusiastic (or scared) engineers who say it will replace us all, the discourse is polarized. But I’ve been more interested in a different question: what does AI coding actually cost, and what does it actually save?
I recently had Claude help me with a substantial refactoring task: splitting a monolithic Rust project into multiple workspace repositories with proper dependency management. The kind of task that’s tedious, error-prone, and requires sustained attention to detail across hundreds of files. When it was done, I asked Claude to analyze the session: how much it cost, how long it took, and how long a human developer would have taken.
The answer surprised me. Not because AI was faster or cheaper (that’s expected), but because of how much faster and cheaper.
I’ve seen a very few analyses like this, and I think it’s really important. Here Tarek Ziadé a very experienced software engineer working on the Mozilla codebase. Documents in detail. His work on a real-world piece of software engineering using Claude code. And the impact it had in terms of costs and time saved. The things that went well and the things that maybe went less well.
