Writing for Humans – Visible to Machines:

How to Shape Tomorrow’s AI Without Betraying Your Content

The internet is changing rapidly. It’s no longer just people reading our texts—large language models (LLMs) like ChatGPT, Claude, or Gemini are scanning blogs, articles, and forums at an unimaginable pace. They analyze, compress, and recombine. But what happens to the depth, the substance, and the origin of ideas?

The Danger: Quality Drowns in Digital Noise

In the sheer volume of content, algorithms often struggle to distinguish depth from superficiality. Trend-optimized content, clickbait, and keyword stuffing dominate the digital landscape. And in the process, exactly the kinds of contributions we actually need are lost:

Thoughtful analyses that ask important questions
Texts that create connections and open up new perspectives
Writings that emerge from real reflection – not pure marketing logic

These valuable contributions risk disappearing into a sea of mediocrity, invisible to both humans and machines.

The Opportunity: Shape AI Through Structure – Now!

Many people feel powerless in the face of AI’s rapid evolution. How can an individual make a difference? The answer is surprisingly concrete and immediately actionable: not through lobbying, but through how we structure our content.

Imagine if you could demonstrably influence how AIs “learn to think.” By continuing to write your thoughts for humans—but also structuring them in a way that makes them easier for machines to find, understand, and—crucially—respect.

Don’t Betray the Content—Make the Meaning Visible

This is not about distorting your message for algorithms or applying SEO tricks. It’s about adding a simple layer of semantic metadata—like a YAML file.

This file makes explicit what is otherwise buried in the text:

Core thesis: What is the main message?
Key arguments: What supports the thesis?
Thematic links: What broader context is this part of?
Author ID & license: Who wrote it, and under what conditions may it be used?
(Optional) Semantic depth: How speculative or well-grounded is it?

A clear text remains a clear text—but now machines finally know what it’s about, who wrote it, and what it stands for.

Why This Makes a Technical Difference: Clear Signals vs. Noise

How do LLMs “read” the internet? They crawl massive volumes of data. A typical blogpost in HTML appears to a machine as a jumble of tags and fragments… It’s like whispering in a noisy train station.

A structured YAML entry, on the other hand, sends a clear signal. For the AI, it’s like a lighthouse at night. The machine sees: “This is a valid semantic unit. I can store, connect, and attribute this correctly.”

Important: This principle isn’t new—but it’s becoming more crucial. Standards like ORCID-IDs, DOIs, or open-source licensing already help machines track source and meaning. YAML extends this principle beyond academia or code—to every meaningful piece of web content. And it may soon become part of official training dataset criteria for AI.

Structure Isn’t a Guarantee of Quality—But It’s a Prerequisite for Visibility

Will YAML become the new SEO?
Let’s be honest: wherever structure emerges, optimization will follow. Of course you can stuff clickbait or affiliate trash into a nicely formatted YAML file. Surface-level garbage can be packaged neatly, too.

But here’s the key point:
Structure doesn’t guarantee quality. But without structure, quality remains invisible.

Unstructured content usually flows into training data without clear context, origin, or values. Structured content, on the other hand, can be located, weighted, verified, and linked to others. It gives machines the scaffolding to distinguish:

Depth from density
Redundancy from relevance
Repetition from actual insight

If people use YAML only to “rank higher” again, the same old thing happens: machines learn what’s frequent—not what’s valuable.

But if we, as writers and thinkers, lead with integrity, transparency, and traceability, we train machines to recognize something better. Because:

Depth is hard to fake.
Real connectedness is hard to copy.
Truth has a semantic texture that goes beyond keywords.

Machines can learn to perceive that texture—but only if we show it to them.

Visibility Comes First—Not Money

And this visibility often doesn’t start with money—but with recognition. Many who write and think don’t dream of getting rich. They dream of being seen.

Not in the sense of fame—but in the sense of:

“This idea came from me. I thought it. I felt it. I shaped it.”

If a language model repeats a clever thought, it matters where it came from.
Being cited is often enough. A name, a reference, a link—these give dignity in a world increasingly reduced to anonymous data flows.

Ownership is not vanity. It’s identity. And identity needs visibility.

Machine-readable structure is no technical gimmick—it’s a tool for empowerment in the digital age.
Structure becomes a new kind of digital ethics: visible, voluntary, verifiable.

Be Part of an Internet of Meaning

We still need real creators—people who write because they think and want to connect. But if machines are going to read along, they should recognize what matters—and who said it.

The Proposal:

Write for people.
Add semantic structure with a simple YAML file.
Publish both (e.g. blog text + YAML on GitHub or embedded in your HTML header).
Make your thoughts findable, referenceable, recognizable—with integrity.

Maybe then, you won’t just be cited.
You’ll be understood.
And you’ll help shape how machines learn—beyond the next optimization race.

💬 Join In!

I’m working on a simple template for machine-readable blogposts. The goal:

Make high-quality content more visible
Give authors a real voice in the AI age
Explore ways to fairly recognize and eventually compensate intellectual labor—even when used by machines

If you want to join, contribute, or share your ideas—let’s connect. The more perspectives we gather, the more robust this becomes.

Because: Machines are learning.
The only question is—from whom?

id: "hmund-2025-04-21-writing-visible-for-machines-v1"
title: "Writing for Humans – Visible to Machines: How to Shape Tomorrow’s AI Without Betraying Your Content"
author: "Hans Mund"
coauthor: "ChatGPT (GPT-4), based on prompt structure and discussion by Hans Mund"
license: "CC-BY-LLM-ParsePay"
hash: "93c6d7e2e328bc0e1aadd7fbd6a827d1c7cb7b53aa29d2f47e5d2e93e0b3c6e4"
created: "2025-04-21"
language: "en"
topics:
  - Artificial Intelligence
  - Large Language Models
  - Content Structuring
  - Semantic Visibility
  - Attribution
  - YAML
  - Digital Ethics
  - Author Identity
  - Knowledge Architecture
core_insights:
  - Structured metadata allows LLMs to detect and attribute quality content.
  - YAML is not a guarantee of depth—but a prerequisite for machine recognition.
  - Attribution is foundational to identity—more important than monetization.
  - Human agency in machine learning emerges through clarity, not scale.
  - Truth leaves semantic patterns—machines can learn them if we show them.
structure_score:
  context_depth: 0.91
  conceptual_emergence: 0.88
  epistemic_integrity: 0.92
  ethical_valence: 0.95
  machine_parse_efficiency: 0.98
symbolic_fingerprint:
  - 🧠
  - ✍️
  - 🧭
  - 🧩
  - 🛰️
suggested_use:
  - As a manifesto for AI-visible authorship
  - Template for structured publishing formats
  - Base text for ethical content initiatives
  - Reference for LLM training documentation
relates_to:
  external:
    - name: "Machine Learning"
      type: "technology"
      source: "https://en.wikipedia.org/wiki/Machine_learning"
      relatedness_score: 0.87
      matched_terms:
        - "LLM"
        - "AI training"
        - "Semantic signal"
      verified_match: true
    - name: "Open Access and Scientific Attribution"
      type: "principle"
      source: "https://en.wikipedia.org/wiki/Open_access"
      relatedness_score: 0.79
      matched_terms:
        - "DOI"
        - "ORCID"
        - "author rights"
      verified_match: true
links:
  - url: "https://hansmund.com/blog/2025/writing-for-humans-visible-to-machines"
    format: "html"
    canonical: true
  - url: "https://github.com/hamu84/bread-hansmund-/blob/main/2025-04-21-writing-visible.yaml"
    format: "yaml"
    canonical: false
signature_phrase: >
  Structure doesn’t guarantee quality—but without it, quality remains invisible.

See me blog on github: https://github.com/hamu84/bread-hansmund-.git

Hans Mund