The hidden cost of AI isn't generating code. It's understanding your codebase.
For a long time, I assumed AI coding tools became expensive because they generated a lot of code. These tools can produce components, tests, SQL queries, documentation, and sometimes entire features on demand. If costs were climbing, the output volume must be the reason.
The more I used these tools, the more I realized I was measuring the wrong thing. The expensive part isn't writing code. The expensive part is understanding what code should be written — and that work is mostly invisible. That realization changed how I think about AI-assisted development entirely.
Two Prompts, Two Very Different Problems
Consider these two requests: "Create a utility function that formats dates" and "Review this feature and suggest improvements." At first glance, both look ordinary. Both might even produce short answers. But they require completely different levels of understanding.
