What happens when building the requirement becomes cheaper than describing it?

Software projects did not begin with prose because prose was perfect. They began with prose because implementation was expensive. Requirements, diagrams, backlogs, and prototypes were ways to reason about a system before paying the full cost of building it.

The problem is that stakeholders often approve descriptions they interpret differently. The disagreement is real, but it remains hidden until the future system has enough behavior to reveal it.

AI does not remove ambiguity. It changes how cheaply some ambiguity can be turned into a working workflow. A team can now build a credible claims path, approval flow, or renewal process, let people operate it, and revise it during the conversation.

That means scope can increasingly come from shared experience. The organization is not only asking "Do you agree with this requirement?" It is asking "Is this what you meant when the software behaves this way?"

The RDD shift is from interpreted scope to experienced scope.

The economic benefit comes from moving consequential disagreement earlier. A wrong approval stage, missing role, misunderstood exception, or unnecessary dashboard is cheaper to change in a disposable working model than after architecture, implementation, testing, integration, and UAT.

This is not a universal promise of dramatically lower budgets. Production systems still require engineering. RDD aims to enter that work with clearer intent, stronger scope boundaries, better evidence, and less avoidable rework.