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How AI Training Data Scraping Works—And Why Publishers Can't Track It
•Erik Svilich, Founder & CEO | Encypher | C2PA Text Co-Chair

How AI Training Data Scraping Works—And Why Publishers Can't Track It

AI models are trained on billions of web pages, including your content. Here's how the data collection pipeline works and why traditional tracking methods fail.

#AITraining#WebScraping#Copyright#DataCollection#ContentProtection

By: Erik Svilich, Founder & CEO | Encypher | C2PA Text Co-Chair

Every major AI language model—GPT-4, Claude, Gemini, Llama—was trained on text scraped from the internet. 󠇟󠇠󠇡󠇢󠇬󠄜󠄲󠆵󠅦︇󠄲󠇪󠅻󠆅󠄦󠅼󠇂󠅩󠅉󠅩󠅗︃󠅯󠄦󠇑󠇀󠄦󠅴󠆜󠇣󠆔󠇢󠆴󠆅󠇦󠅐󠆿󠅌󠆫󠇫︂󠄮󠇒󠇌Billions of web pages, millions of articles, countless hours of human creativity and expertise, all collected and processed to teach machines to write. 󠇟󠇠󠇡󠇢󠆔󠄣󠆧󠅝󠆤︇󠄿󠇒󠆋󠄢󠇞󠄣󠅿󠅮󠅸︂󠇂󠅴󠅱󠆤󠄎󠅅󠅣󠆉󠇤󠅥󠆺󠅱󠄬󠄴󠄕󠇣󠆠󠆟󠄙︄󠅅󠇮󠇟󠅼If you've published content online, it's almost certainly in one of these training datasets. 󠇟󠇠󠇡󠇢󠄹󠄦︅󠄀󠅛󠇭󠄷󠄬󠆔󠇧󠄙󠇐󠅃󠅣󠇡󠆀󠇣󠅡󠆈󠄰󠄐󠅉󠅤󠄦󠇪︄󠄤󠆋󠄁︇󠄺󠄶󠆟󠄍󠇊󠇢󠅈󠄱󠅠󠅵And you probably have no idea. 󠇟󠇠󠇡󠇢󠇜󠄯󠄧󠅨󠆒󠄜󠄶󠄞󠅳󠆯󠅇󠆄󠄫󠆜󠄶󠇘󠄏󠄢󠄗󠅘󠅒︀󠆏󠆣󠅂󠅕󠄐󠇤󠄓󠅗󠆳󠇋󠇁󠄥󠆊󠇌󠇠󠄃󠅫󠄅## 󠇟󠇠󠇡󠇢󠆐󠇐󠅉󠇜󠆆󠄃󠄿󠅈󠅴󠆅󠇧󠆜󠅗󠅀󠅔︊󠅉󠆭󠆫󠇡󠇩󠄡󠄰󠇀󠄕󠅋󠆆󠄔󠇐󠄄󠄲󠆥︎󠄜󠄋󠅣󠆼󠆕︌󠅒The AI Training Data Pipeline

Understanding how AI companies collect training data reveals why publishers have so little visibility into—or control over—how their content is used.

Stage 1: Web Crawling

The foundation of most AI training datasets is web crawling—automated programs that systematically browse the internet, downloading and indexing web pages. 󠇟󠇠󠇡󠇢󠇎󠄚󠆆󠇙󠆺󠅝󠄱󠆟󠆏󠇛󠄧󠆶󠆺󠄆󠅱︆󠇓󠆞󠄉󠇂󠅼󠇈󠆲󠄋󠆖󠇢󠇑󠄕󠆣󠆺󠇖󠅶󠆀󠄶󠄱󠄤󠅺󠅮󠅖󠆟Common Crawl: The Internet's Archive

The most widely used source is Common Crawl, a nonprofit that has been archiving the web since 2008. 󠇟󠇠󠇡󠇢󠄶󠄣󠄑󠆖󠄻󠄎󠄱󠅛󠅹󠆌󠄅󠅽󠆗󠆣󠆒󠇤󠅰󠇤󠆮󠇚󠅊󠄑󠄆󠇆󠇤󠄲󠅍󠄸󠄧󠆄󠇜󠆄󠆫󠆧󠇉󠄓󠅺󠇅󠆶󠆾Key facts:

  • 250+ billion pages archived
  • 3-5 billion pages added monthly
  • Petabytes of data freely available
  • Used by virtually every major AI lab

Common Crawl doesn't discriminate. 󠇟󠇠󠇡󠇢󠄬󠅹󠄗󠄋󠇊󠇥󠄰󠄏󠆇󠅢︄󠅩󠄏󠄠󠄴󠆎󠇢󠄩󠇧󠄠󠅁󠆖󠆏󠆗󠇍󠆤󠄲󠇡︊󠆋󠇔󠅣󠄸󠅃󠄺︆󠆉󠇠󠆕󠄀It crawls news sites, blogs, forums, social media, academic papers, books, and everything else publicly accessible on the web. 󠇟󠇠󠇡󠇢󠅰󠅁󠅽󠇚󠅗󠇋󠄰󠅅󠅵󠅁󠆏󠅁󠆪󠆠󠆠󠅛󠇕󠄞󠇧󠇪󠆥󠆖󠇪️󠆐󠅺󠆥󠄇󠆀󠄴󠆜󠇑󠆈󠄸󠆛󠄾󠇔󠄚󠆂󠅼AI Company Crawlers

Beyond Common Crawl, AI companies operate their own crawlers:

Company Known Crawlers
OpenAI 󠇟󠇠󠇡󠇢󠅍󠅖󠄜󠅂󠇆󠇯󠄲󠅭󠆍󠇍︂󠇁󠅥︃󠆂󠆧󠄽󠄈󠄷󠅣󠅿️󠄜󠄗󠅢󠄒󠇊󠅋󠆵󠆑󠅠󠇥󠆷󠄠󠄌󠅿󠄒󠄂󠇖󠇫 GPTBot
Google Google-Extended
Anthropic ClaudeBot
Meta Meta-ExternalAgent
Apple Applebot-Extended 󠇟󠇠󠇡󠇢󠇩󠄴󠆺󠆁󠅱󠅧󠄸󠄊󠅴󠇤󠅻️️󠆏󠄘󠅥󠄖󠆣️󠅦󠅀󠅭󠆼󠄨󠅵󠆔󠇪︋󠄄󠆰󠅴󠅌󠆙󠆔󠇋󠄟󠇊󠅉󠆆󠅮

Stage 2: Dataset Curation

Raw web crawls contain enormous amounts of noise—spam, duplicates, low-quality content, and non-text data. 󠇟󠇠󠇡󠇢󠄻󠅗󠄹󠇒󠇘󠅟󠄼󠄥󠆄󠅌󠆫󠆽󠄿󠄐󠆡󠇆󠆇󠄲󠆜󠄳󠅷󠆂󠆶󠄜󠆨󠇞󠄺󠄥󠄝󠇁󠅷󠅧󠇯󠅭󠄻󠇭󠆵󠄘󠄂󠇔AI labs process this raw data into curated datasets. 󠇟󠇠󠇡󠇢󠄜󠄤󠇘︆󠄪󠆥󠄱󠆤󠆤︈󠅢󠆤︌󠆎󠅄󠅫︎󠅈󠆫󠆄󠅄󠄿󠆽󠆔󠅡󠆻󠆣󠆼󠆸󠆋󠆚󠅮︂󠆛󠅖󠇫󠆿󠆾󠅿󠆨The Pile

One of the most influential open datasets, The Pile, includes:

  • Books3 — 196,640 books (many copyrighted)
  • OpenWebText2 — Reddit-linked web content
  • Wikipedia — Full English Wikipedia
  • PubMed Central — Scientific papers
  • ArXiv — Academic preprints
  • GitHub — Code repositories
  • Stack Exchange — Q&A content

Refinement Process

Curation typically involves:

  1. 󠇟󠇠󠇡󠇢󠇯󠄳󠇋󠄘󠅬󠄤󠄷󠄄󠆚󠆓󠇜󠆩󠄸󠄔󠄎︆󠄹󠅐󠅟󠇇󠆎󠆶󠄷󠆮󠄻󠄄️󠅽󠅫󠄍󠅧󠄻󠄞󠄨󠅮󠆹󠆏󠄙󠇃️Deduplication — Removing exact and near-duplicate content
  2. 󠇟󠇠󠇡󠇢󠆔󠇬󠆃󠅴︈︆󠄱︎󠅽󠄖󠆎󠅇󠆙󠆌󠆶󠅷󠄤󠆬󠄚︊󠆿󠄬󠄏󠄔󠆞󠅋󠆱󠄹󠆢󠄉︃󠆨󠇊󠇆󠅂󠅎󠆍︄󠇤󠅨Quality filtering — Removing spam, boilerplate, and low-quality text
    • 󠇟󠇠󠇡󠇢󠆧󠇜󠄁󠅐󠆤󠅧󠄺󠆰󠅵︄︅󠆸󠄶󠆅󠄔󠆪󠆼︅󠅛️󠇘󠇛󠅕󠄺󠆂󠄫󠅶󠆦󠄬󠆎󠆵󠄺󠅃󠄘󠆣󠄾︃󠄽󠆝󠄢Language filtering* — Selecting specific languages
  4. 󠇟󠇠󠇡󠇢󠄩󠄁󠅣󠅎󠄲󠅛󠄷󠆋󠅲︊󠅓󠄘󠅃󠆇󠆽󠇢󠄍󠅎󠄅󠆨󠇟󠆐󠅌󠇀󠄥󠄖󠄃󠄢󠄍󠆦︃󠇧󠇃󠄊󠅟󠇯󠄓󠆷󠅀󠅕Content filtering — Removing explicit or harmful content
  5. 󠇟󠇠󠇡󠇢󠅵󠆤󠆝󠆇󠅔󠅛󠄻󠇏󠆚󠇮󠅜󠅿󠆭󠄮󠆣󠇔󠆺󠅻󠅤󠆲󠅳󠄏󠆌󠇔󠅫󠇁︆󠇆󠇯󠆩󠄹󠇏󠆍󠇠󠆨󠆐︂󠄄󠆥󠆌Format normalization — Converting to consistent text format

During this process, metadata is stripped. 󠇟󠇠󠇡󠇢︂󠅺󠆻󠆄󠄕󠄈󠄿󠄏󠅷󠅉󠇛󠄢󠅕︃󠄙󠅷󠅻󠄋󠆲󠇡󠄾󠅾󠆢󠄻󠆃󠅍󠆠󠅠󠆯󠅑󠇛󠇬󠄃󠆡󠇄︀󠄰󠆿󠇧󠄖Author names, publication dates, copyright notices, and source URLs are typically removed or separated from the content itself. 󠇟󠇠󠇡󠇢󠅆󠄶󠄒󠅖󠅚󠆵󠄷󠇉󠆪󠅶󠆙󠇑󠇌󠅱󠄔󠇗󠇈󠆦󠇧󠄚󠆓︁󠄊󠆀󠆎󠄧󠅭󠇐󠇀󠆥󠆍󠅭︂︈󠅲󠆙󠇛󠆿󠆃󠄣### Stage 3: Training

The curated dataset is used to train the model through a process that fundamentally transforms the data:

  1. 󠇟󠇠󠇡󠇢󠇠󠇓󠅬󠆣󠄲󠄖󠄱󠄒󠆊󠅧󠄙󠄦󠇜󠄥󠆰︎󠇮󠆔󠆇󠄦󠅿󠄶󠄄󠆶󠅅󠆒󠆊︌󠆌󠄰󠅶󠇏󠅀󠅽󠅚󠄸󠆋󠆖󠄹󠆡Tokenization — Text is broken into tokens (word pieces)
  2. Embedding — Tokens are converted to numerical vectors
  3. 󠇟󠇠󠇡󠇢󠇍󠇠󠇡󠆖︊󠆬󠄵󠄂󠆞󠇃󠅈󠅡󠇏󠄆󠄝󠆪️󠄡󠅕󠅝󠅆󠅑󠆺󠆙󠄧󠄰󠄙󠇆︇󠇣󠇬󠇏󠄢󠇤󠄒󠇨󠇈󠅛󠅩󠅪Training — The model learns patterns across billions of examples
  4. Optimization — Weights are adjusted to minimize prediction error

After training, the original text doesn't exist in the model in a retrievable form—but the patterns, knowledge, and even specific phrasings are encoded in the model's parameters.

Stage 4: Deployment

The trained model is deployed for inference—generating new text based on user prompts. 󠇟󠇠󠇡󠇢󠆛󠇐󠇪󠅈󠆿󠅍󠄲︇󠆣󠄯󠇡󠆕󠄏󠅱󠄺󠅱󠆣󠆄󠆇󠄦󠆏󠇇󠆻󠆸󠇨󠅊󠅿󠅩󠅽󠄣󠆖󠇔󠆳󠅜︋󠆊󠄈󠆏󠇕󠅺At this stage:

  • The model may reproduce training data verbatim (memorization)
  • 󠇟󠇠󠇡󠇢󠆊󠄊󠅃󠆏󠅅󠇑󠄹󠅐󠅿󠄒󠆃️󠅏󠆳󠇕󠇎󠄯󠆀󠅐󠄢󠇯󠅞󠅊󠆇󠄞󠄟󠇅󠆃󠄡󠆾󠅞󠇚󠅡󠅘󠄻󠆉󠆸󠆡󠇇󠆯The model may paraphrase or combine training sources
  • The model may attribute information to sources ("According to the New York Times...")
  • There's no reliable way to trace outputs back to specific training inputs

Why Traditional Tracking Fails

Publishers have tried various methods to track and control their content. 󠇟󠇠󠇡󠇢󠆤󠄣󠆡󠄕︆󠆳󠄺󠄮󠆃󠅳󠆎󠆾󠄔󠄕󠄊󠄳󠄪󠅟󠇡󠄽󠇓󠅺󠆙︊󠄅︊󠄰󠆮󠅖󠆋󠇕󠇔󠆨󠆬󠅀󠅥󠇔󠆉󠆋󠄳None work reliably against AI training pipelines.

Robots.txt: The Gentleman's Agreement

The robots.txt file tells web crawlers which pages they shouldn't access. 󠇟󠇠󠇡󠇢󠅚󠆘󠄄󠇎󠆇󠄤󠄺󠇟󠆈󠄦󠅹󠄵󠆤󠄛󠅼󠆝󠄱󠆐󠆪󠆧󠇁󠄤󠇫󠇭󠆒󠅱󠄰󠄭󠅩󠅹󠇙󠄔󠇁󠄟󠆇󠆔󠆑󠇃󠇑󠇐The problem:

**It's purely voluntary. ** 󠇟󠇠󠇡󠇢󠇑󠄰󠄵󠆢󠆚󠆱󠄹󠇭󠆅󠆲󠇔󠄜󠆎󠇭󠆖󠄃󠆷󠇃󠆸󠇇󠄓󠇃󠆖󠆘󠅮󠆪󠇎󠆅󠄈󠅒󠅦󠄛︇󠆄󠄐󠅩󠆁󠅋󠇙󠅤󠇟󠇠󠇡󠇢󠅅󠅿󠄪󠄔󠇤󠇬󠄱︂󠆤️󠅇󠅺󠆼󠅸󠄗󠆘󠄌󠅊󠅯󠇆󠄧󠄗󠄜󠆅︎󠄴󠆺︆󠄣󠆆󠇎󠅵󠄒󠄧󠄞󠄷󠄠󠆿󠆻󠅄There's no technical enforcement. 󠇟󠇠󠇡󠇢󠆌󠅹󠄜󠇡󠇭󠄽󠄳󠅍󠆎󠆺󠆄󠄾󠄞󠄯󠄡󠆾󠄰󠄏︍󠆑󠅫󠅂󠄮󠆖󠇓󠄔󠄣󠅟󠇄󠅇󠇁󠅎󠄥󠄰󠇩󠇕󠅧󠄔󠄴󠆝Crawlers can simply ignore it. **󠇟󠇠󠇡󠇢󠅨󠇔︎󠅇󠅡󠆦󠄳󠆭󠅳󠆱󠆥󠅵󠇪󠅀󠅾󠄑󠇑󠆀󠅓󠅚︌󠇤󠇈󠆿︋󠅫󠇢󠄺󠇄󠇫󠆾󠇪󠇆󠄗󠅎󠆆󠄠󠄌󠅁󠅻It's often ignored. * * 󠇟󠇠󠇡󠇢󠆴󠄐󠇖󠆦󠄟󠇩󠄻󠄵󠆪󠄚󠅐󠇕󠄦󠅱󠇈󠆓󠄝️󠆈󠄄󠇊󠄸󠄘󠅅󠅐󠄢󠆤󠅰󠅣󠆛󠄛󠅓󠄺󠆎󠅺󠆎󠆌󠇐󠆸󠅦󠇟󠇠󠇡󠇢󠄾󠆆󠅧󠅐󠆬󠄯󠄻󠅟󠅻︀󠄊󠅻󠅹󠄱󠅰󠆔󠅄󠅁󠄖󠄙󠄳󠄻󠆂󠇐󠅺󠇨󠇀󠅵󠇃󠆵󠆳󠆼󠅉󠄙󠄣󠄤󠆡󠄲󠆔󠇠󠇟󠇠󠇡󠇢󠇃󠅙󠅛󠄠󠆇󠇃󠄴󠆵󠆕󠆗︄︂󠇦󠄙󠆫︉󠅁󠇔󠆳󠇨󠅲︁󠄬󠅇󠄌󠄁󠄃󠇗󠇬󠅦󠄣󠄧󠅿󠆀󠄏󠄏󠅄󠅺󠅑󠇬Investigations have shown AI company crawlers accessing content despite robots.txt restrictions. **󠇟󠇠󠇡󠇢󠇕󠄻󠄍󠅨󠆞󠄍󠄸󠇝󠆢󠅕󠄺󠄮󠇉󠆢󠄙󠄿󠅏󠆰󠇈󠇒󠇫󠅱󠄳󠆝󠆇󠄜󠇃󠆠󠆊󠆫󠄶󠅳󠅬󠇊󠅳󠇜󠇧󠇒󠄐󠅲It's retroactive. ** 󠇟󠇠󠇡󠇢󠆦󠄆󠇕󠅎︂󠄻󠄵󠆽󠅲󠆪󠄝󠄂󠆄󠆔󠇒︁󠅾︂󠅁󠇬󠇎󠄀󠄂󠅃󠄯󠇦󠅡󠅦󠄒󠆕󠄧󠅕󠆾󠄲󠅁󠆧󠆂︎󠆲󠅿Content crawled before you added restrictions is already in datasets. **󠇟󠇠󠇡󠇢󠆶󠄓󠆪󠄅󠅾󠆁󠄱󠄢󠆩󠄉󠆾󠆃󠇏󠄌󠇞󠅣󠄈󠄚󠇂󠄇󠇡󠄅󠅕󠆰󠆩󠆟󠆷󠆓󠇕󠇂󠆰󠇬󠅁︇󠇮󠇚︆󠄊󠇬󠇘It's all-or-nothing. ** 󠇟󠇠󠇡󠇢󠆿󠅦󠆍󠇩󠇆󠇍󠄷󠄦󠆥󠅮󠄶󠆳󠆆󠇘󠄊︃󠆱︈󠅸󠄞󠅓󠄰󠇬󠆺󠄗󠄞󠆕󠆯󠅇󠆠󠇕󠅯󠆐󠄁󠆛󠆝󠄢󠄪󠆌󠆩You can't say "crawl for search indexing but not for AI training. "

󠇟󠇠󠇡󠇢󠅐󠄈󠇒󠇩󠄢󠅌󠄼󠆸󠆩󠇮󠅠󠅯󠄄󠄼󠇑󠇪󠅠󠇀︋󠄀󠄑󠆴󠅭󠇎󠇃󠄛󠄡󠆗󠇆󠅅󠇒󠆪󠅉󠆈󠄛󠇗󠄶󠇀󠇐󠆡Example robots.txt:

User-agent: GPTBot
Disallow: /

User-agent: ClaudeBot
Disallow: /

This tells GPTBot and ClaudeBot to stay away—but only if they choose to comply.

Paywalls: Leaky by Design

Paywalls seem like they should protect content from scraping. 󠇟󠇠󠇡󠇢󠇉󠅱󠅐󠄺󠇟󠆧󠄵󠅠󠆋󠆪󠇟󠅝󠅵󠇏󠅩󠅏󠇃󠄨︊󠄶󠆪󠆸󠇦󠇕󠆺󠆻󠅄󠅑󠄓󠅽󠅭󠅿󠇃󠅄󠄩︅󠆠󠆜󠇊󠄃In practice:

**Cached versions exist. ** 󠇟󠇠󠇡󠇢󠄴󠅄󠄯󠇛󠆛󠅲󠄼󠆩󠅾󠆱󠄠󠇭󠄡󠆖󠄚󠄹󠄿󠅮󠇗󠇤󠇌󠄣󠇕󠇄󠄱󠄅󠅹󠇏󠅝󠆠󠄦󠄭󠄋󠄔󠄗󠅉󠄶󠄹󠆀󠆈Search engines cache paywalled content. 󠇟󠇠󠇡󠇢󠄤󠄰󠄶󠄼󠅇󠅕󠄻󠅷󠆨󠇕󠄐󠆖󠆏󠇘󠇍︊󠅇󠄥︁󠇏󠇭󠇚󠄧󠇌︃︍󠇓󠅲󠅒󠅂󠅢󠆉󠄰󠇤︈󠆤󠄬󠄣︄︌Archive services preserve it. 󠇟󠇠󠇡󠇢󠆍󠆊󠆺󠆥󠇌󠅴󠄲󠆧󠆇︊󠅜󠆯󠆮󠆙󠆕󠄠󠅤󠄥󠆜󠆅󠆞󠇮󠆠󠅝󠇦󠅎󠅲︃󠅏󠆸󠄑󠅜󠆺󠄿󠆷󠄭󠅐󠇢︎󠇬These caches are crawlable. **󠇟󠇠󠇡󠇢󠇍󠇇󠄳󠅛󠅛󠄏󠄷󠆡󠆔󠅱︄󠅖︉󠅒󠄘󠅘󠇪󠄂󠆈󠇀󠄦︋󠆹︎󠆣󠅂󠇈󠅉󠇓󠇦󠇐󠇘󠇫󠄻󠄄󠄋󠆢󠅬󠆛󠄡Metered paywalls leak. ** "󠇟󠇠󠇡󠇢󠆦󠇎󠅟󠆮󠆎󠆷󠄸󠄝󠆀󠄆󠇐󠅯︁󠄱󠅶󠅙󠇭󠅫󠅍󠄡󠄵︎︋󠆼󠆳󠄗󠆆󠄻️󠅀󠇭󠄺󠆙󠇧󠇍󠇄󠅒󠇒󠄇󠄴Read 3 free articles" means those articles are fully accessible to crawlers. **󠇟󠇠󠇡󠇢󠆁󠄞󠄳︍︈󠇎󠄾󠅥󠆪󠆊󠆓󠆶󠇗󠆟󠅹︊󠇟󠇞󠄨󠇯󠅳󠇚󠆨󠅾󠄌󠆦󠄖󠆛󠅓󠅶󠄬󠅳󠄂󠆟󠆸󠇘󠅄󠆧󠆠󠆨B2B distribution bypasses paywalls. ** 󠇟󠇠󠇡󠇢󠇮󠅝󠄔󠄻󠄀󠆓󠄸󠄾󠅼󠅗󠅁󠅲󠆴󠇨︇󠅇󠅱󠄐︍󠅌󠄵︁󠄮󠅎󠅮󠅺󠅺󠅭󠄸󠆎󠅌󠄴󠆫󠄙󠄼󠅬󠅔󠅃︅󠆄Content licensed to other publishers often appears without paywall protection. **󠇟󠇠󠇡󠇢󠅬󠅷󠆶󠇕󠅤󠆨󠄶󠆎󠆣󠆭󠄝󠇬︎󠇛󠆂󠄎󠆩󠆎󠆪󠆡󠅦󠇝󠅜󠆕󠇚󠄂󠆕󠄖󠅉󠄏󠅡󠅚󠆐󠄕󠇆󠆑󠆻󠄶󠅯󠄒Social sharing creates copies. ** 󠇟󠇠󠇡󠇢󠄢󠆣󠆭󠇒󠇉󠇮󠄿︄󠆒󠄉󠆡󠄋󠅬󠄗︎󠇉󠆕󠄭︎󠇡󠄞󠇣󠆉󠇦󠄅󠄇󠇆󠅂󠅮󠆱󠇡󠇌󠄴󠅻󠆰󠄻󠆕󠄖󠅑󠄸Users share excerpts, screenshots, and full text on social platforms. 󠇟󠇠󠇡󠇢󠄎󠇊󠇮󠇩󠅀󠇝󠄴󠇏󠆘󠄣󠇮󠆮󠄰󠅥󠆞󠆠󠄏󠅝󠆶󠅠︀󠇐󠅹󠄐︀󠄊󠅨󠇚󠄝󠇔󠄤󠇭󠅒󠆵󠄼󠅛󠇒󠇥󠅥󠇢### Copyright Notices: Invisible to Machines

That © symbol and copyright statement at the bottom of your page? 󠇟󠇠󠇡󠇢󠅨󠆫󠇈󠆥󠆧󠇞󠄻󠄱󠆈󠆪󠇝󠄭︇󠇪󠇬󠆙󠄲󠅴󠆰󠄮󠆼󠅗󠇝󠆑󠇟󠄡󠇔󠆄󠆇󠄁󠅧󠆻󠅺󠅊󠆑󠄺󠆟󠆙󠄻󠆊It's:

**Stripped during processing. * * 󠇟󠇠󠇡󠇢󠆽󠅡󠆾󠆝󠆔󠄋󠄽󠄓󠆒󠅇󠆌󠇚󠄮󠇨󠅠󠅃️󠄜󠇟󠆄󠅉󠄯︋󠇖󠅬󠇝󠅒󠅵󠅈󠄳󠄭󠆬󠇏󠄵󠅜󠅲󠅰󠇕󠆔󠇒󠇟󠇠󠇡󠇢󠅞󠅪󠆱󠆹󠅏󠆡󠄲󠆱󠅸󠅺󠅑󠇬󠆗󠄛󠆽󠅊󠅮󠅮󠆌󠇞󠄌󠅢󠄡󠅬︊󠇈︉󠆼󠅗󠆵󠇂󠆀󠅱󠅄󠆵️󠅐󠆜󠆘󠄘Dataset curation removes boilerplate, including copyright notices. **󠇟󠇠󠇡󠇢󠄼󠆼󠄄󠇠󠄅󠄿󠄺󠄛󠆯︈󠇋󠄐󠆩󠅵󠄍󠅀󠆜󠄍󠆞󠄶󠄁󠄂󠅎󠅶󠄮󠅗󠇘󠇨󠅈󠄳󠅍󠆮󠆌󠅫󠆳󠆐󠆸󠄫󠄏󠄩Not machine-actionable. ** 󠇟󠇠󠇡󠇢󠅊󠇟󠄶󠄁󠅸󠄓󠄶󠆊󠆋󠆷󠄊󠄲󠇗󠇕︆󠆖󠅱󠄃󠅄󠅶󠆴︀󠄎󠆭︅󠅖︁󠆃󠆞︆󠅳󠆖󠆳󠆄󠆪󠆟󠇪󠆯󠅶󠇙󠇟󠇠󠇡󠇢󠇩󠄍󠅈󠅄︃󠄙󠄸󠆢󠅿󠅸󠄩󠇄󠅐󠄰󠄠︌󠇙󠆭󠆈󠄒󠆓󠇒󠇏󠅋󠅔󠄱󠅐󠄓󠅟󠇃󠅽󠄆󠄇󠄗󠅦󠄗󠆨󠆧󠄙󠇓A copyright notice doesn't tell a crawler what it can or can't do. **󠇟󠇠󠇡󠇢󠅈󠄢󠅅︆󠅹󠅞󠄺󠅠󠅶󠆻󠆫󠇔󠅆󠄮󠆌󠇚󠄮󠅖󠆮󠅃󠄤󠄿󠅲󠅁󠄦󠄌󠅗󠅱󠆀󠅢󠇓󠇊󠄆󠆲󠄕󠅆󠅋󠄄󠇧󠇭Separated from content. ** 󠇟󠇠󠇡󠇢󠄰󠄞󠅟︌󠆛󠆖󠄲󠅫󠆈󠆏󠅞󠅙󠅫󠄟󠄿󠇊󠄁󠄶󠅿󠆔󠄪󠅎󠅊󠅢󠆋󠇧󠆰󠄹󠄓󠅯󠄽︋󠆱󠅪󠄃󠄜󠄞󠆩󠄘︌󠇟󠇠󠇡󠇢󠆦󠇍󠆏󠅾󠅀󠅉󠄼︄󠆍󠅾󠆜󠄻󠄧󠆥󠇯󠅏󠅒󠅺󠅖󠇅󠇡︊󠅅󠇗󠆴󠇮󠅴󠄰󠇁󠅝󠅧︀󠇓󠆫󠇩󠇀󠅁󠄡󠇘󠄣Even if preserved, the notice isn't linked to specific content in the dataset.

Terms of Service: Unenforceable at Scale

Your terms of service may prohibit scraping for AI training. 󠇟󠇠󠇡󠇢󠄇󠅄󠄏󠆆󠆹󠇤󠄺󠄼󠆐󠇟󠅑󠆢󠆫󠅎󠅈󠅊󠅀󠇪󠇀󠆈󠇐󠆨󠄹󠆕󠆕󠄳󠆺󠄺󠅳󠆃︉󠇙󠇈󠄨󠅓󠆟󠇁󠆨󠄑󠄞But:

Crawlers don't read ToS. 󠇟󠇠󠇡󠇢󠅚󠅩󠆊󠄴󠄔󠆘󠄻󠅃󠆀󠆾󠇛󠆌󠅦󠇕󠄑󠇠󠇐︊󠆸︎󠅨󠆩󠅓󠆤󠅽󠇚󠆓󠆀󠅞󠅊󠅟󠄻󠅲󠇎󠇧󠅚󠄅󠄠󠆍󠄥Automated systems don't parse legal documents. **󠇟󠇠󠇡󠇢󠄇󠄆󠅺󠅨󠇪︆󠄻󠆐󠆩󠅧󠄁󠅩󠇫󠇃󠅩󠆰󠄥󠄣󠄙󠆓󠇥󠆙󠅶󠄟󠅓󠆱󠅱󠄹󠆭󠅮󠄙󠅏󠄫󠅺󠇪󠆧︅󠄇󠅪󠇑Enforcement requires detection. ** 󠇟󠇠󠇡󠇢󠅍󠅈󠄲󠆛󠄆󠅊󠄹󠇣󠆧󠆄󠇋󠄏󠆵󠆈󠄉󠄱󠄅󠅋󠇠󠄓󠇀󠆨󠄾󠅻󠄜󠅩󠄘󠆒󠅈󠅜︀󠄥󠇅󠆷󠇆󠇇󠅉󠆉󠇥󠄠You can't enforce terms against activity you can't detect. **󠇟󠇠󠇡󠇢󠅸󠄡󠄠󠄃󠄙󠅞󠄰󠅪󠅻󠆧󠅍󠆊󠅩󠄎󠄠󠆲︌󠅊󠅵󠇬󠆄󠆤󠆍󠄅󠇚󠄬󠄆󠆵󠆭󠅩󠄦󠇣󠄥︉󠇈󠄻󠅶󠆧󠆌︈Jurisdiction is complex. ** 󠇟󠇠󠇡󠇢️󠇆󠅵󠄝󠇡󠅞󠄾󠄒󠆔󠇡󠅣󠄣󠆤󠅢󠇟󠄍󠅛󠄞󠆚︉󠇨󠇅󠅵󠆏󠄈󠆗󠆥󠆶󠄓󠄾󠇩󠆋󠇒󠇔󠇆︀󠇎︆󠄣󠅺AI companies operate globally; your ToS may not apply.

Analytics and Tracking: Blind Spots

Traditional web analytics tell you about human visitors, not crawlers:

**Crawlers don't execute JavaScript. ** 󠇟󠇠󠇡󠇢󠅡󠆝󠄄󠄳󠇠󠄏󠄶󠄔󠆊󠄃󠄭󠇦󠅦󠄫󠄶󠄈󠇎󠄺󠆥󠆖󠇈󠆯󠆽󠄤󠆖󠄀󠇏󠇠󠄋󠆹󠇃󠇖󠆔󠄅󠇁󠄑󠆴󠄛︈󠆆󠇟󠇠󠇡󠇢︃󠅊󠆌󠇍󠆧󠄛󠄸󠇄󠆨󠆽󠅕󠆃󠇂󠇢󠇠󠅎󠅧󠄽󠄶󠄿󠅱󠅢󠄝󠅎󠆲󠄏󠅀󠇄󠅚󠄅󠆻󠆗󠆎︇󠅹󠄻󠅥󠆬󠅑󠄏Most analytics rely on JavaScript that crawlers ignore. **󠇟󠇠󠇡󠇢󠅰󠆜󠅐󠅀󠆽󠄞󠄴󠄵󠆉󠄱󠇎󠅷󠄜󠅾󠄜󠇢󠆚󠆔󠄸󠅠󠆂󠄥󠅽󠄪󠇧󠄩󠄒󠆴󠆔󠅊󠇄󠄩󠄤󠇛󠅡󠄹󠇦󠄳󠅒󠄞User-agent spoofing is common. ** 󠇟󠇠󠇡󠇢󠄛󠄭󠄠󠄅󠆛󠆌󠄻󠇖󠆂󠇒󠇝󠆚︉󠆂󠄖󠇚󠄂󠄔󠆐󠄸󠅭󠆡󠇃󠄨󠄎󠆹󠄄󠆾󠇯󠅛󠅈󠅳󠇜󠄲󠅶󠄑󠆙󠅘󠇞󠅄Crawlers can disguise themselves as regular browsers. **󠇟󠇠󠇡󠇢󠅬󠇃󠄎󠇦󠄙󠄧󠄽󠅓󠅼󠄛󠆘󠅡️󠆜󠇓󠇠󠆡󠅂󠅓󠇁󠆹󠄧󠆻󠆷󠇉󠅥󠄂󠄇󠇅󠅼󠅝󠄪󠆿󠆳󠅟󠅥󠅟󠄎󠅭󠆗Server logs are incomplete. ** 󠇟󠇠󠇡󠇢󠆝󠆂󠅼󠅎󠄊󠆸󠄱󠇌󠆊󠅡󠆍󠅡󠄜󠄮󠅑󠇊󠆸󠆈󠅎󠆮󠇅󠄉󠅒󠅸︌󠆂󠅵󠄠󠄧󠇤󠆪︈󠄰󠅠󠄠󠄃󠇧󠇭󠆁󠄸󠇟󠇠󠇡󠇢󠄯󠇃󠄿󠇨󠇓︍󠄴󠆺󠆆󠇍󠆷󠅊󠇖󠄳︂󠆰󠄪󠆠󠆹󠄌󠆼󠇫󠄨󠄭󠄮󠇞️󠅜󠄄󠇔󠄁󠅔󠆂󠅬󠄁󠆶󠆧󠅛󠆾󠇪Not all crawling activity appears in standard logs. 󠇟󠇠󠇡󠇢︍󠄊󠄘󠅚󠆧󠅂󠄼󠆯󠆍󠄹󠇐󠄴󠇁󠅼󠆢󠇤󠅶󠆼󠆳󠆩󠅍󠅭󠅩󠆭󠆑󠇣󠆎󠇒󠇧󠆼󠆘󠅙󠅑󠇜󠆡󠅸󠆥󠄦󠅰󠆏## 󠇟󠇠󠇡󠇢󠆛󠇗󠆯󠆛󠄹󠄎󠄵󠄾󠆢󠅪︅󠆘󠅶󠇗󠅸󠄴󠇭󠄹󠆟󠄝󠆦󠆕󠄿󠆌󠅭󠅣󠅁󠅽󠆨󠆁󠆨󠇪󠅪󠄎󠅜󠆄󠄌󠄽󠇟󠆑The Scale of the Problem

To understand why this matters, consider the scale:

󠇟󠇠󠇡󠇢󠇇󠇚󠄣󠇋󠄘󠄷󠄴󠄵󠆂󠇏󠄐󠇯󠅔󠄈︉󠅇󠆡󠄱󠄰󠆧󠆴󠅊︀󠄻︅󠄂󠅉︀󠄊󠄛󠆎󠇮󠆬󠇡󠄑󠅹󠆼󠅥󠄹󠇃What's in GPT-4's Training Data?

OpenAI hasn't disclosed GPT-4's full training data, but estimates suggest:

  • Trillions of tokens of text
  • Content from millions of websites
  • Books, articles, papers, code, and conversations
  • Data collected through 2023 or later

󠇟󠇠󠇡󠇢󠅼󠄇󠅌󠄺󠆕󠇕󠄳󠄅󠅾󠅻󠆞󠅦󠆬󠇑󠅳󠄶󠄏󠄅󠆺󠅓󠇘󠄨󠆓󠆠󠇭󠇤︇󠄁󠆍︎󠆚󠆈󠆆󠆶󠇢󠆥󠅘︁󠅒󠄌What's in Common Crawl?

As of 2024, Common Crawl contains:

  • 250+ billion web pages
  • Over 100 petabytes of data
  • Content from virtually every major website
  • Archives going back to 2008

󠇟󠇠󠇡󠇢󠄅󠇭󠅭󠆌︁︄󠄽󠄐󠆨󠅗󠅷󠄃󠄳󠅀󠇏󠇋󠄼󠅣󠆑󠆲󠆠󠅔󠅅󠇮󠆑󠄗󠅖󠄃󠄛󠄤󠅯󠅿︂󠇖󠅣󠇭󠆢󠄲󠄈󠅰The Math for Publishers

If you're a major publisher with:

  • 100,000 articles online
  • Average 1,000 words per article
  • Published over 10 years

That's 100 million words of content—almost certainly in multiple AI training datasets, being used to train models that compete with your content for reader attention. 󠇟󠇠󠇡󠇢󠅓󠇦󠆤󠄊󠇩󠇑󠄵󠅯󠆀󠄘󠅦󠄓󠅊󠄧󠇒󠄡󠆔󠅣󠅺󠄄󠆉󠇥󠄼󠄥︀󠄌󠇙︃󠄑󠅠󠅂󠅅󠅧︍󠆼󠆟󠄣󠇞󠆪󠆸## 󠇟󠇠󠇡󠇢󠆿󠄐󠅏󠆈󠅾󠆐󠄽󠆐󠅴󠆑󠅔󠅟󠅼󠅶󠆣󠇖󠄔󠄤󠅢󠅽󠇨󠅨󠆂󠇮󠄥󠆪󠆕󠄔󠅥󠅲󠅡󠇟󠆫󠄟󠄂󠅄󠆮󠄏󠄜󠆑What AI Companies Know (and Don't Know)

Here's the uncomfortable truth: **AI companies often don't know exactly what's in their training data. **

󠇟󠇠󠇡󠇢󠇜󠄟󠆎︈󠅣󠆄󠄶󠆋󠅶󠇞󠆬󠆁󠅚︁󠆣󠅿󠅦󠇮󠆉󠆝︈󠄮󠇬󠇉󠇡󠅥󠄸󠇍󠄓󠅦󠆀󠄃󠆵󠄰󠄟󠆨󠆗󠆿󠆹󠆖### 󠇟󠇠󠇡󠇢󠇕󠆺󠇯󠇃󠅀󠄳󠄳󠄁󠅰󠅣󠄋󠅦󠅼󠆥󠆗󠄂󠄟󠆪󠅌󠅟︁󠄧︆󠇫󠆀󠅗󠆥󠄿󠆂󠄍󠆳󠅼󠄭󠅽󠆭󠆴󠅜󠆻󠆹󠅇The Knowledge Gap

** 󠇟󠇠󠇡󠇢󠅲󠆢󠆚󠆑󠆆󠇛󠄾󠅑󠆁󠆸󠇥󠇯󠆿󠆝︂󠅿󠄧󠄦󠄣󠄴󠅐󠄏󠇨󠇪󠄓󠅘󠆖󠅹󠄋󠆒󠇌󠅬󠄌󠆌󠄔󠄞󠇢󠄧󠄺󠆖They know the sources** — Common Crawl, Books3, etc. 󠇟󠇠󠇡󠇢︀󠅚󠅪󠆮󠄿󠅕󠄹󠆢󠆕󠄑︆󠇉󠅞󠆓󠄓󠄫󠄽󠄒󠆎󠅾󠄅󠇆󠆟󠄗󠆡󠅴󠄬󠆼󠅾󠇃︊󠄷󠆊󠇮︁󠅯󠆽󠇎󠇃󠆺They don't know the specifics — Which articles from which publishers on which dates. 󠇟󠇠󠇡󠇢󠇎󠇫󠅆󠇆󠇢󠅱󠄿󠅏󠆅󠆅󠅐󠅰󠄡󠅵󠄰󠄺󠅼󠇄󠇩󠆣󠅕󠅵󠆆󠆿󠇀󠆞󠅬󠆦󠅌󠇖󠅆󠆘󠅓󠅨󠄎󠄦󠅉󠇖󠅒󠄝They can't trace outputs — When a model generates text, they can't identify which training examples influenced it. 󠇟󠇠󠇡󠇢󠅘󠆝󠄣󠆪󠇇󠅿󠄾󠆃󠆆󠄞󠅎󠆔󠅃󠄡󠆣󠆟󠄡󠄅󠄱󠅏︀󠅝󠇞︋󠅱󠆃󠆠󠅠󠅾󠄁󠄺󠅊󠅂󠄗󠇈󠅡󠅐󠅾󠄚︂They can't remove specific content — "Unlearning" specific training data is an unsolved research problem. 󠇟󠇠󠇡󠇢󠅨󠇮󠇢󠆚󠆀󠅃󠄵󠇨󠅰󠄻󠆬󠆷󠇉󠄜︊󠇓󠄻󠅒󠅧󠅔󠄃󠄬󠅡󠇞󠆊󠆚󠅢󠄋󠅃󠆘︈󠆹󠅿󠇀󠇩󠄵󠇧󠄭󠇭󠄡This creates the "we didn't know" defense: AI companies can truthfully claim they didn't specifically know your content was in their training data, because the scale makes specific knowledge impossible.

󠇟󠇠󠇡󠇢󠆜󠆍󠇒󠄴󠅟󠅈󠄵󠄽󠆨󠇕󠇌󠇘︄󠅻󠇋󠆤󠅖󠅥󠆪󠆁󠆉󠆟󠄡󠄝︄󠆾󠅂󠅠󠇎󠄻󠅆󠄕󠅜󠇏󠇤󠇃󠅤󠅽󠅮󠄞The Provenance Solution

The only way to solve this problem is to make content self-identifying—to embed proof of origin into the content itself so that it travels through any pipeline.

How Cryptographic Provenance Works

Instead of relying on external signals (robots.txt, copyright notices, terms of service), cryptographic provenance embeds proof directly into the text:

  1. 󠇟󠇠󠇡󠇢󠆡󠄲󠇫󠆳󠇜󠇠󠄻󠄥󠆓󠇨󠅄󠆺󠄆󠄵󠇝󠄴󠇇󠅗󠆶󠇧󠄖󠇬󠆿󠇜󠇌󠆃󠅾󠄄󠄏󠇆󠄏󠆍︀󠆃󠄺󠅭󠅾󠄘󠅹󠇫At publication, content is signed with a cryptographic signature
  2. 󠇟󠇠󠇡󠇢󠆀󠄺󠅤󠆪󠇋󠄊󠄴󠄣󠆒󠇑󠇟󠄇󠆽󠇧󠅓︁󠆫󠅍󠄙󠇤󠆛󠄰󠅏︊󠅉󠅯󠆼󠇛󠄩󠅛︎󠄩︊󠆕󠇯󠅡󠆑󠆓󠅤󠇧The signature is embedded using invisible Unicode characters
  3. 󠇟󠇠󠇡󠇢󠄓󠄖󠅩︂󠇓󠇍󠄹󠇑󠆗󠆭󠅮󠆟󠅞󠆮󠄂󠅍󠄗󠆖󠇘󠇓󠇬󠅟󠇇󠅎︂󠅆︇󠅉󠅐󠇏︈󠄲󠆂󠅸󠆆󠆁󠄹󠆳󠆗󠇓Through any transformation—copying, scraping, processing—the signature persists
  4. 󠇟󠇠󠇡󠇢󠇌󠆏󠆿󠇪󠄅󠇑󠄰󠅯󠆖︉󠄸󠆗󠇖󠄃󠅌󠄺󠅊󠄆︉󠆊󠅭󠅀󠄼󠅊󠅥󠆅󠆴󠆼󠇋󠅂󠆭󠇃󠅖󠆮󠅉󠇮󠅴󠇀󠇎︀At any point, the signature can be verified to prove origin

What This Enables

Detection: AI companies can detect marked content in their training pipelines. 󠇟󠇠󠇡󠇢󠅽󠅄󠅱󠄈󠆐󠆨󠄹󠅹󠅿󠄇󠄞󠆥󠄟󠄘󠆳󠄝󠇥󠆓󠄞󠆃󠅙󠅶︍󠇞󠄻󠆰󠇗󠇛󠄋󠅂󠆂󠅱󠅞󠇑󠇢󠄤󠄧󠅌󠅨󠄂Attribution: 󠇟󠇠󠇡󠇢󠄌󠅡󠆙󠄜︌󠆛󠄰󠆃󠆢󠆽󠇐󠄼󠅨󠆞󠆈󠅮︈󠆺󠄿󠆒󠆟󠆕󠇎󠇉󠆮󠇩󠄠󠆷󠅡󠅫󠇤󠇪󠄦󠇞︎󠅱󠄎︊󠅀󠇪The origin of content can be cryptographically verified. 󠇟󠇠󠇡󠇢󠇨󠄆󠇖󠄭󠆎󠅘󠄲󠆺󠆋󠅋󠇑󠆁󠅂󠇯󠆔󠆼󠆲󠆿︅󠆘󠅻󠄻󠆢󠇉󠅮︁󠇜󠆘󠅿󠆄󠆧󠅅󠄊󠆈󠅊󠅀󠆱󠅊󠄾󠆚Notification: 󠇟󠇠󠇡󠇢󠆧󠇕󠅣󠅳󠄄󠇝󠄷󠇕󠆤󠅆󠅸󠆣󠄺󠄈󠅼󠅁󠅃󠇓󠆤󠇒󠆂󠇯󠄊󠆗󠄲󠅌󠇥󠅼󠅼󠅏󠆈󠄵󠅩󠆊󠄮󠆬󠇨󠅴󠆼󠄑Publishers can formally notify AI companies that their content is marked. 󠇟󠇠󠇡󠇢󠆆󠇢︋󠄯󠇁󠆗󠄲󠆖󠆦󠆟󠄁󠆞󠄸󠅾󠆌󠄲󠄀󠅝󠄁󠅒󠆸󠅞󠇌󠄍󠅈󠇅󠄹󠅾︀󠆘󠅹󠅓󠆵󠄝󠅒󠅼󠇇󠇑󠄴󠅙Accountability: "󠇟󠇠󠇡󠇢󠄕󠅑󠅤󠆢󠆼󠄇󠄸󠄷󠆬︌󠄌󠄯󠆨󠅗󠄹󠅰󠅨󠇯󠇈󠄭󠅘󠅛󠅱󠆍󠆜󠄴󠆱󠆤󠅟󠆅󠆽󠄄󠅣󠇊󠆟󠆝󠄭󠆣󠄝󠄈We didn't know" becomes "you ignored our notice. "

󠇟󠇠󠇡󠇢󠆨󠄔︂󠇛󠇑󠄁󠄲󠇔󠆃󠅗󠄎󠇄󠇪󠅎󠅢󠆅󠅡󠇁󠅕󠆽󠅫󠇜󠅝󠄎󠆺󠇆󠅆󠆀󠅍󠇡󠄽󠆹󠆾󠇪󠆣󠄼󠅘󠄁󠅻󠅩## 󠇟󠇠󠇡󠇢󠆶󠅨󠆛󠇭󠆋󠅏󠄿󠆀󠅴󠇋󠅈󠅭󠆝󠇧󠄧󠆲︅󠆚󠄪󠆾󠅚󠄩󠄈󠄛󠄌󠇉󠇣󠄵󠄈󠇓󠇚󠆫󠆫󠅈︌󠄬󠄜󠆙󠆱󠅃What Publishers Should Do

Short Term

  1. 󠇟󠇠󠇡󠇢󠇌󠆹󠅉󠅦󠆫󠇟󠄽󠆨󠆤󠄏󠅮󠇕󠅙󠄉󠄝󠄤︎󠇜󠅣󠄕󠄎󠅓󠅄󠅦󠅳󠆤󠆴󠆾󠇡󠄖󠅯󠅩󠄤󠅽󠆪󠄠󠅷󠅉󠆗󠅿Implement robots.txt for AI crawlers (GPTBot, ClaudeBot, etc.)—it's not perfect 󠇟󠇠󠇡󠇢󠇨︇󠇘󠇩󠆔󠇢󠄼󠆁󠅸󠆪󠅆󠆡󠄐︊󠆔󠄡󠆌︄󠆌󠆹︉󠅈󠆏󠅊󠆪󠆊󠇮󠇌󠆰󠆢󠆈︍󠄶󠅞󠄷󠄼󠆼󠅒󠇄󠇚but it's a signal of intent
  2. 󠇟󠇠󠇡󠇢󠆮󠄄󠄉󠄬󠄓󠇨󠄱︈󠆮󠅿󠆀󠄍︅󠅬︃󠄅󠆸󠄘󠇀󠆾󠅚󠆊󠅹󠅋󠆾︄󠆇󠅤󠆱󠄟󠅤󠆔󠆚󠆫󠆳︊󠇜︃󠅣󠄱Document your content with timestamps and archives for potential legal action
  3. 󠇟󠇠󠇡󠇢󠅢󠄽󠅻󠅿︋󠆷󠄱󠅲󠆫󠆺󠆜󠅥󠄄󠇅󠇫󠅒󠅺󠅄󠆛󠅂󠅾󠇂󠇒︃󠅎󠇊󠇙󠆵󠄗︎󠇡󠅏󠄻󠇣󠅾󠄐󠇫󠇮󠇃󠆡Monitor for memorization by testing AI models for verbatim reproduction of your content

Medium Term

  1. 󠇟󠇠󠇡󠇢󠄬︆󠇑󠇫󠅾󠅝󠄲󠅡󠆁󠇘󠆺󠇦󠆱󠄲︈󠅆︃󠄾󠄒󠅍󠄗󠆺󠅖︄󠄭󠇒󠅑󠅧︂󠆱󠅍󠅿󠅳󠅼󠄽󠆞󠄹󠅛󠅩󠆪Evaluate provenance solutions that embed proof of origin into content
  2. 󠇟󠇠󠇡󠇢󠇦󠆁󠄻󠄃󠇉󠄠󠄺󠄲󠆐󠆑󠅄󠆫󠆥󠆬󠅂󠆕󠆇󠆚󠅳󠆟󠅧󠄠󠅬󠆺󠇄󠇁󠄚󠇗󠇄󠅧󠄹󠆵󠅑󠇬󠄐󠅆󠆣󠅤󠆂󠅧Prepare licensing frameworks for AI training data
  3. *󠇟󠇠󠇡󠇢󠆯󠇯󠆨︀︅󠆄󠄲󠅄󠆦󠄯󠄪󠅿󠄅󠅕󠆺󠅹󠅄󠅧󠄦󠅖󠅠󠄃󠅨󠄋󠆺︎󠆦󠆏󠆶󠆙󠆊󠅒󠅶󠅪󠇓󠇄󠆡󠅈󠅵󠇎Join industry coalitions * working on content attribution standards

Long Term

  1. 󠇟󠇠󠇡󠇢󠇆󠆴󠆰󠆿󠅑󠅜󠄺󠄱󠆊󠇣󠇠󠄛󠄪󠄗󠆝󠇬󠅁󠅅󠇖︇󠄅󠄄󠄋󠆁󠄩󠅩󠅈󠄖󠆿󠆽󠆌󠇬󠅱󠆥󠇒󠇓󠆤󠅤󠅣󠅁Implement cryptographic provenance across your content
  2. 󠇟󠇠󠇡󠇢󠄃󠅉󠇎󠄉󠄞󠆽󠄴󠆧󠅿󠆫󠄤󠆂󠆘󠅆󠄮󠄃󠆱󠄲󠅼󠆌󠆞󠅁󠇕󠄐󠄁󠄇󠅔󠇡󠄡󠅅󠅐󠇒󠄹󠄻󠇪󠇀︅󠆯󠇂󠄰Establish formal notification processes for AI companies
  3. 󠇟󠇠󠇡󠇢󠄡︆󠇘󠅟󠆴󠆧󠄺󠅐󠆢󠇑󠇂️󠅜󠄮󠅫󠆄󠇭󠅨󠇓󠅘󠆷󠄉󠄜󠆽󠇕󠅒︀󠄌︅󠆦󠆾󠆼󠄭󠆒󠇗󠆗󠅹󠄼󠄅󠆾Build licensing infrastructure to monetize AI training usage

󠇟󠇠󠇡󠇢󠆫󠆭󠅪󠆐󠇦󠅘󠄲󠄤󠅽󠄎󠄠󠄙︁󠅃󠅌󠅈︅󠅱󠅄󠆞󠆓󠆝󠄫󠄢󠆟︆󠇒︀󠆦󠇀󠆁󠄣󠆓󠆷󠅑󠇢󠇭󠅑󠆇󠇒The Path Forward

The AI training data pipeline was built in an era when content on the web was assumed to be freely usable. 󠇟󠇠󠇡󠇢󠅉󠅠󠅻󠅺︄󠄄󠄽󠆝󠆃󠅄󠆚󠆕󠇨󠇌󠄍󠄯󠇦󠅌󠅥󠄘󠅫󠅶󠅏󠇗󠅄󠄍️󠆾󠄛󠅚󠄆︎︇󠇝󠇐󠅗󠆜︃󠄉󠆞That assumption is being challenged legally, ethically, and technically. 󠇟󠇠󠇡󠇢󠄠󠆇󠅟󠆈󠅹󠅮󠄳󠅓󠆤󠆲󠇃︌󠅻󠆿󠆠󠇔󠅃󠅿󠆷󠅉󠄬󠇩󠇄󠅵󠅦󠅤󠇕󠆣️󠆬󠄴󠄆󠆓󠅔󠅅󠄔󠇫󠇦󠅎󠆨Publishers who understand how this pipeline works—and implement solutions that work within it—will be positioned to protect their content and participate in the AI economy on their terms. 󠇟󠇠󠇡󠇢󠄁󠇔󠄹󠅉󠄇󠆚󠄱︇󠆄󠄖󠅃︁󠄯󠅃︆󠇛󠅳󠄺󠇍󠅡󠇊󠆅󠄪󠄳󠅄󠇞󠄼󠇚󠄙󠆾󠇃󠆲󠆄󠇌󠅾󠆚󠇤󠆛󠄳󠆦The alternative is to remain invisible: your content training AI models, your attribution stripped away, your rights unenforceable because you can't prove what was taken. 󠇟󠇠󠇡󠇢󠅡︉󠆔󠆥󠆧󠄗󠄹󠅁󠆐󠅈󠇠󠅬󠄨󠅈︊󠆟󠄠󠄿󠅫󠄉󠆕󠅠󠅋󠄴󠆱󠄅󠆅󠅷󠄄󠇔󠄈󠄗󠅐󠅷󠇝󠇠󠅜󠇨󠆻󠄛Learn more about content provenance for publishers: 󠇟󠇠󠇡󠇢󠄳︉󠇖󠅱󠄰󠇔󠄸󠇕󠆅󠇓󠅒󠅴󠅞︈󠆦󠇇󠇝󠇝󠅴󠅫󠇥󠄳︊󠆮󠅀󠄁󠆠󠆴󠅟󠇮󠅡󠇡󠄐󠅞󠅺󠇎󠄚󠇪󠆠󠄀encypherai.com/publisher-demo

#AITraining #WebScraping #Copyright 󠇟󠇠󠇡󠇢󠅠󠇢󠅁󠄉󠇃󠆱󠄹︄󠆛︎󠇝󠆎󠆣︆︎󠇎󠆙󠅵󠄗󠅭󠅛󠄷󠄿󠄧󠄎󠄹󠅽󠇭󠅆󠅦󠅟󠆳󠄬󠇘󠄗󠆞󠅞󠆟︈󠄫#DataCollection #ContentProtection󠇟󠇠󠇡󠇢󠇀󠅟󠇌󠆠󠇑󠆮󠄰󠄜󠆇󠄗󠅞󠄫󠄃󠅪︂󠅫󠅨󠄤󠆲󠄢󠅴󠇙︄󠅱󠆩󠄌󠇫󠆤󠅗󠄱󠄫󠇋󠄛󠆩󠄾󠄅󠅻󠇙󠄚󠇜

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