Markdown is for Thinking. HTML is for Showing.

Editor's take

AI-powered tools are struggling to produce documentation that stakeholders can effectively use, leading to a proposed two-stage workflow.

This disconnect highlights a fundamental challenge in AI deployment: bridging the gap between raw generation and actionable intelligence. Developers and product managers, accustomed to structured formats like HTML for clear presentation, find AI-generated Markdown specs dense and difficult to parse for decision-making. The issue isn't the AI's ability to generate content, but its failure to anticipate the downstream consumption needs of human collaborators, impacting project velocity and clarity.

Future developments should focus on AI models trained not just on data, but on the *context* of its use. Specifically, we'll need to see if AI can reliably adapt its output format to specific user roles and workflows, moving beyond generic Markdown to produce tailored, readily digestible specifications for varied audiences. The success of this proposed two-stage process hinges on its seamless integration and the AI's ability to learn and apply these formatting preferences with minimal human intervention.