While tokens are an LLM's energy source, structured markdown files in a system like Obsidian act as its perfect, persistent memory. This organized, interlinked data is the true "oxygen" that allows an AI to develop a deep, evolving understanding of your context beyond single-session interactions.

The simple, text-based structure of Markdown (.md) files is uniquely suited for both AI processing and human readability. This dual compatibility is establishing it as the default file format for the AI era, ideal for creating knowledge bases and training documents that both humans and agents can easily use.

Storing information in simple, portable formats like Markdown is crucial for future-proofing data in the AI era. This approach mirrors the brilliant design decision of using plain text for HTTP, which drove its widespread adoption.

The traditional competitor for B2B tools was an Excel spreadsheet. In the AI era, it's a simple, version-controlled Markdown file within an IDE. If a SaaS offering for documentation or project management can't provide more value than this highly flexible, interoperable setup, it will lose.

Instead of a complex database, store content for personal AI tools as simple Markdown files within the code repository. This makes information, like research notes, easily renderable in a web UI and directly accessible by AI agents for queries, simplifying development and data management for N-of-1 applications.

Markdown, originally designed for blogging, has emerged as the de facto standard for interaction between LLMs and tools. This happened not by design, but because it's human-readable, highly token-efficient compared to alternatives like HTML, and familiar to the early adopters who trained the models.

Notion's journey to a working AI agent involved multiple failed attempts. Key lessons were to stop forcing models to use Notion-specific data formats and instead provide them with familiar interfaces like Markdown and SQLite, which they are pre-trained to understand well.

Engineering AI tools understand markdown better than complex PRDs in other formats. Product leaders can translate critical user workflows into simple markdown files, providing context to the AI to help it analyze the impact of code changes and identify potential issues.

Consolidate key company information—brand voice, copywriting rules, founder stories, and playbooks—into structured markdown (.md) files. This creates a portable knowledge base that can be used to consistently train any AI model, ensuring high-quality output across applications.