Part I: The Origin of AIFC
2. The First Pain: Markdown Helped AI, But Started to Hurt Human Attention
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Once the same pattern was used in multiple projects, a new problem appeared.
Markdown solved the AI continuity problem, but created a human attention problem.
There were more and more files.
They contained:
- raw human ideas,
- AI-generated summaries,
- refined AI text,
- partially reviewed sections,
- fully approved sections,
- outdated notes,
- duplicated context,
- speculative ideas,
- decisions,
- assumptions,
- and long narrative explanations.
At first, this seemed manageable.
Then it became hard to answer basic questions:
- What is my original input?
- What did AI generate?
- What did I already review?
- What did I approve?
- What is still draft?
- What is outdated?
- Which part of the file is reliable?
- Which part is only AI interpretation?
- Which file should be updated?
- Which file is the current source of truth?
- Which file is duplicate?
- Which section matters now?
Markdown gave the knowledge a body.
But it did not yet give it lifecycle, ownership, status or trust.
This led to a crucial realization:
A text file can be readable and still not be human-manageable.
The problem was not only storage.
The problem was attention.