There is a lot of work that goes on before even reaching the point to write code.

For example, being able to vibecode a UI wireframe instead of being blocked for 2 sprints by your UI/UX team or templating an alpha to gauge customer interest in 1 week instead of 1 quarter is a massive operational improvement.

Of course these aren't completed products, but customers in most cases can accept such performance in the short-to-medium term or if it is part of an alpha.

This is why I keep repeating ad nauseum that most decisionmakers don't expect AI to replace jobs. The reality is, professional software engineering is about translating business requirements into tangible products.

It's not the codebase that matters in most cases - it's the requirements and outcomes that do. Like you can refactor and prettify your codebase all you want, but if it isn't directly driving customer revenue or value, then that time could be better spent elsewhere. It's the usecase that your product enables which is why they are purchasing your product.

As a researcher in formal methods, I totally get you