Model Collapse Is Coming: Why AI Companies Need Verified Training Data

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

There's a problem brewing in AI that few outside the research community are talking about: model collapse. 󠇟󠇠󠇡󠇢󠅝󠇚󠄺󠇗󠆉󠆉󠄸󠇀󠆚󠆸󠆂󠇆󠆳󠆅︈󠄋󠆾󠅟󠆐󠄪󠅳󠄡󠆠︊󠅅󠇦󠄦󠅭󠆏󠆨󠄲󠅄󠅯󠆹󠇕󠆰󠅻󠆐󠅉󠇊As AI-generated content proliferates across the internet, future AI models will increasingly train on synthetic data created by previous AI models. 󠇟󠇠󠇡󠇢󠄘󠅌󠆂󠇖󠅝️󠄴󠇜󠆐󠆙󠆃󠆥󠅂󠇉󠆥󠄇󠆾󠅸󠇬󠇞󠆭󠅝󠅖󠄒󠅽󠆜󠆢󠄟󠄠󠆞󠇆󠄩󠆰󠅝󠇩󠆳󠇎󠇔󠅶󠇚The result is a gradual degradation of model quality—a feedback loop that threatens the foundation of generative AI. 󠇟󠇠󠇡󠇢󠆭󠆨󠇑󠄤󠆎󠄤󠄾󠅢󠆔󠅶󠆤󠆍󠄈󠆴󠄙󠅱󠄻󠇨󠆐󠄴󠄧󠅞󠆵󠇜︍󠆔󠆛󠅎󠅬󠅉󠄽󠇘󠅤󠅑󠄗󠅣󠆍󠅳󠄦󠆛The solution? 󠇟󠇠󠇡󠇢󠅩󠄙󠆗󠅷󠆽󠆽󠄴󠅂󠆮󠆋󠅾󠅰󠆙󠇂︌󠇆󠇪︁󠅞󠆯󠆒󠇍︌󠇒󠆦󠄑󠄁󠅄󠄢󠇮󠄄︋󠇞󠅺︁󠇯󠄿󠆷󠅉󠄍Verified human-created content becomes the most valuable resource in AI. 󠇟󠇠󠇡󠇢︁󠅦󠅉󠆪︁󠅑󠄽󠄊󠅽󠄙󠄏󠇑󠅆󠆙󠄱󠆠󠄗󠅰󠄃󠅪󠆼󠆖󠅼󠄻󠆄󠆣󠆣󠅖󠇟󠅢󠇠󠇝󠆉󠅈󠄜󠇟󠄄󠄹︅󠆬And that changes everything for publishers. 󠇟󠇠󠇡󠇢󠅦󠆑󠇐󠆞󠄈󠄶󠄵︌󠆣󠇤󠆳󠅲󠇖︃󠆂󠆄󠄅︇󠄌󠅇󠄋󠆵󠇩󠆝󠆛󠆮󠇔󠆫󠇄󠆑󠆧󠅓󠆮󠅖󠅘󠄪󠆒󠅪󠅜󠅝## 󠇟󠇠󠇡󠇢󠇂󠆱󠅉󠇆︍︍󠄹󠇔󠆨󠅣󠅯󠆃󠇘󠆌󠅎󠄿󠆲󠆝󠄼󠆪︅󠆊󠇋󠅮󠇦󠅔󠆥󠆵󠆀︌󠆝󠅋󠅔󠆊󠆾󠇑󠄛󠅴󠇝󠆻What Is Model Collapse? 󠇟󠇠󠇡󠇢︇󠄊󠆴󠄤󠆗󠅔󠄼󠆋󠅶󠆫󠄭︅󠆞󠅋󠆳󠅕󠄈󠆤󠅌󠅶󠆅󠆘󠅧󠅪󠇏󠇙󠄍󠄵󠆝󠇄󠅱󠅠󠇔󠆫󠄑󠆓󠆅󠆴󠆆󠄅Model collapse occurs when AI models are trained on data that includes significant amounts of AI-generated content. 󠇟󠇠󠇡󠇢󠆦󠄔󠄉󠆬󠆡󠄗󠄿󠅼󠆧󠄞󠄸󠇅󠅫󠅙󠅱󠄼󠄱󠆉󠅤󠅪󠄆󠅣󠇊󠇠󠄥󠅖󠇐󠇠󠆧󠄌󠄹󠅁󠆇󠇡󠄶󠇠󠄌󠆖󠄗󠆍Each generation of models trained this way performs slightly worse than the last, eventually degrading to the point of uselessness. 󠇟󠇠󠇡󠇢󠇋󠄆󠅫︁󠄿󠄍󠄾󠆦󠆣󠆲󠄊󠅺󠆯󠆑󠄛󠅭󠅂󠆝︁󠄀󠄊󠄶󠄣󠄊󠅓󠇂󠆭󠅨︌󠅨󠅲︅󠄶󠆾󠇎󠅐󠇢󠅹󠇮󠄋### The Research

A landmark 2023 paper from researchers at Oxford, Cambridge, and other institutions demonstrated model collapse empirically:

"󠇟󠇠󠇡󠇢󠇢󠅢󠇇󠅅󠇐󠇁󠄺󠇀󠆠󠄞󠄩󠆊󠅭󠅾󠄍󠅰󠅽󠄔󠄂󠅗󠇔󠄎︌󠆛󠇞󠅤󠆲󠆌󠄢󠄈󠇫󠇑󠅇󠅥󠄒󠇟󠄏󠅜󠆠󠆓We find that use of model-generated content in training causes irreversible defects in the resulting models... 󠇟󠇠󠇡󠇢󠅱󠆫󠆕󠅬󠆋󠅍󠄷󠆁󠆡󠄁󠅼︆󠇇󠄽󠆫󠆢󠅚󠄰󠇌󠅽󠅟󠅦󠆰󠅃󠆭󠄏󠄠󠇝󠇧󠄮󠆫󠅼󠇖󠄏󠆥󠄜󠇧󠆺󠄲󠄕The tails of the original content distribution disappear. "

󠇟󠇠󠇡󠇢󠅥󠅃󠆚󠆲󠅦󠄧󠄶󠆝󠅾󠅒󠆕󠅹󠇬󠆙󠅅󠇕󠆃󠅚󠆖󠅫󠇇󠄇󠆀󠆓󠇏󠅘󠆤󠄺󠅄︊︌󠄾󠆥󠆃󠅺󠆕︍󠅊󠅣󠅞In simpler terms: when AI trains on AI, it loses the diversity, nuance, and edge cases that make language models useful. 󠇟󠇠󠇡󠇢︁︌󠆼󠆭󠇯󠄵󠄴󠅌󠆫󠅆󠅮󠄓󠆃󠆸󠅘󠅲󠇊󠆘󠄘󠆞󠄳󠄠󠆳󠆝󠄍󠅴󠄟󠇄󠄕︌󠄮󠇯󠄙󠇦󠆳󠅊󠄂󠅚󠅿︍### The Mechanism

Here's how it works:

1. ** 󠇟󠇠󠇡󠇢󠅧󠇏󠇃󠇯󠅈󠅝󠄻󠇆󠆏󠆜︄󠄞󠆸󠆸󠅿󠄘󠅃󠄰󠆔󠄟󠄚󠄾󠄑󠄘󠅨󠆸󠄗󠅌︇󠅕󠇬󠆚󠅓󠅷󠇈󠇈󠆼󠄽󠆌󠅑󠇟󠇠󠇡󠇢󠄠󠇤󠄍󠅩󠅋󠇌󠄰󠆔󠆀󠄭󠄾︀󠆏󠅍󠇂󠄍󠅕󠇔󠄥󠄋󠆕󠆨󠄫󠅦󠅨󠄖󠇮󠄯󠅥󠅀󠇂󠆅󠇧󠅕󠇩󠇈󠅱󠄹󠄛󠅯Generation 1:** Model trained on human-created content performs well
2. • 󠇟󠇠󠇡󠇢󠇫󠇥󠅖󠇟󠆀󠇓󠄹󠆪󠅴󠅘󠄥󠅴󠆈󠅛󠇈󠅦󠅍󠅖󠆹󠇆󠄦󠄮󠇭󠄽󠄵󠆦󠄯󠄭󠄾󠄚󠆖󠄴󠅒󠅜󠅖󠇘󠄹󠇫󠆧󠅙Generation 2:* Model trained partly on Gen 1's output loses some diversity
3. ** 󠇟󠇠󠇡󠇢󠆰󠇫󠅆︍󠄿󠄎󠄸󠄬󠅰󠇑󠅽︄󠆍󠆸︊󠅗󠅻󠇌󠇃󠇪󠄐󠆻󠄍︁󠄳󠅚󠄇󠄗󠇤󠆸󠄪󠆱󠅺󠇑󠅉󠇢󠇘󠇕︋︄󠇟󠇠󠇡󠇢︁󠇖󠄧󠄊󠆬󠅜󠄻󠆾󠅶󠅻󠄷󠅟󠆮︅󠇝󠅢󠄴󠆭󠄞󠇡󠄣󠆪️󠄜󠅈󠄽󠅮󠇯󠄁󠄔󠆀󠅯󠄉󠅃󠆎󠇣󠇅󠆬󠄖󠄤Generation 3:** Model trained on Gen 2's output loses more diversity
4. • 󠇟󠇠󠇡󠇢󠆺󠇄󠄖󠆦󠄖󠄦󠄰󠆫󠆨󠇜󠇗󠄾󠄚󠅗󠅖󠆣󠆧󠇂󠅾󠅝󠆺󠇛󠄣󠄒󠆲󠆬󠄝󠅛󠄧󠄷󠄨󠅫󠅮󠇣󠆓󠆀󠅬󠇡󠅰︎Generation N:* 󠇟󠇠󠇡󠇢︉󠇎󠄀󠅬󠅳󠄧󠄿︁󠆄󠇯󠅕󠆦󠅪󠄖󠅠󠅖󠅥󠇮󠄌󠆱󠅈︆󠇯󠅚󠆲󠄉󠆜󠇡󠆈󠄠󠆙󠆐󠆙󠄠󠇀󠆙󠆏󠄹󠄴󠅫Model converges to producing bland, repetitive, low-quality output

Each generation amplifies the biases and limitations of the previous generation while losing the richness of the original human data.

The Math

Researchers found that model collapse follows predictable patterns:

• Variance decreases with each generation
• Rare events disappear from the distribution
• Mean shifts toward the most common patterns
• Quality degrades exponentially, not linearly

After just 5-10 generations of training on synthetic data, models can become essentially useless for many tasks. 󠇟󠇠󠇡󠇢󠅘󠇕󠆲󠇎󠆣󠅲󠄱󠆙󠅻󠅼󠄡󠆨󠄶󠄴󠅴󠄾󠄉󠇘󠇆︎󠅞󠅓󠆕󠆨󠆆󠄥󠇇󠄂󠇘󠄭󠅟󠇝󠄑󠆷󠇃󠄏󠇫󠄿󠆞󠄦## 󠇟󠇠󠇡󠇢󠅷󠄜︉󠆽︃󠇒󠄶󠅽󠆩︇󠅮󠅩󠆊󠅺󠆑︊󠅥︌󠅗󠄛︊󠇌󠆯󠄟󠄞󠅕󠇥󠆎󠅜󠆏󠄽󠅢󠄺󠅼󠅷󠅪󠆗︉󠆕󠇘The Internet Is Filling with AI Content

Model collapse would be a theoretical concern if AI content were rare. 󠇟󠇠󠇡󠇢󠇁󠆺󠄑󠇜󠅥󠄱󠄻󠅘󠅿󠄝󠆝󠄑󠇮󠄚︁󠇉󠅷󠄻󠄒󠆼󠅃󠅙󠆊󠅦󠆙󠄵󠄾󠆄󠆍󠇞󠄜󠅖󠇠󠅹󠆇󠆌󠆲󠆖󠄕󠄛It's not.

󠇟󠇠󠇡󠇢󠇊󠄓󠆑󠇬󠆚︀󠄷󠇜󠆬󠄔󠄏󠅃󠅀󠅅󠄪󠆘󠄳󠅿󠅃󠄒󠆗󠆜󠇠󠄺󠆛󠆣󠇧󠄢󠄠󠄹󠆑󠇃󠇘󠇑󠅵󠆗󠅆󠄌󠅎︃The Scale

Estimates suggest AI-generated content is growing exponentially:

Year	󠇟󠇠󠇡󠇢󠅈󠄛󠆆󠄵󠅥󠅐󠄵󠆝󠆟︋󠅢󠆦󠆵󠄮󠅱󠆆󠆢󠆫︅󠇮󠇫󠆿󠄽󠅸󠅈󠅴󠄉󠄂󠆀󠆚󠅏󠆠󠅳󠇙󠇖󠆔󠅓󠅹󠇗󠇥Estimated % of New Web Content That's AI-Generated
2022	<1%
2023	5-10%
2024	15-25%
2025	30-50% (projected)
2030 󠇟󠇠󠇡󠇢󠄧󠆹󠅃󠇃󠄴󠅑󠄴󠅒󠆀󠆴󠅑󠇀󠆉︄󠇔󠅞󠆵󠄪󠅆󠆬󠆟󠄊󠅁󠆣󠅋󠄽󠅚󠆡󠄿󠇡󠇂󠄠󠅹󠅻󠇇︃󠇖󠇎︇󠆥	90%+ (projected)

Where It's Appearing

AI content is flooding:

• News sites — AI-written articles, often without disclosure
• Blogs and content farms — Mass-produced SEO content
• Social media — AI-generated posts, comments, and profiles
• Product descriptions — E-commerce sites using AI at scale
• Academic papers — AI-assisted or fully AI-written research
• Code repositories — AI-generated code and documentation

󠇟󠇠󠇡󠇢︎󠄞󠅽󠇄󠇁󠆊󠄻󠆖󠆮󠅀︂󠅱󠆫󠆦󠇐󠆂󠅤󠆱󠄽︀󠇔󠇫󠅒󠄳󠄥󠆈󠇩󠅝󠄐󠄐󠇘󠅾󠆣︀󠇔󠄈󠄢󠅄󠅜󠄁The Detection Problem

Here's the catch: you can't reliably detect AI-generated content. 󠇟󠇠󠇡󠇢󠄗󠄫󠇌󠄕󠅰󠆩󠄿󠅻󠆥󠅑󠇂󠄳︄󠅢󠆿󠇆󠅰󠆏󠅧󠅷󠇊󠆲󠄱󠇉󠇂󠄛︍󠆯󠇔󠅶󠄠󠄮󠄌󠄍󠅶󠅍󠅐󠆅󠇐󠆥AI detection tools have accuracy rates barely better than random guessing. 󠇟󠇠󠇡󠇢󠅙󠄋󠇐󠇤󠅌︋󠄹󠆞󠆍󠇫󠆅󠅦󠅗󠄉󠅞󠆚󠅮󠆱󠆇󠆌󠅮󠄹󠄼󠇔󠅁︁󠅪󠆝󠇬󠄝󠄨󠆖󠄤󠇤󠆱󠅸󠄐󠇬󠆧󠇘As AI improves, detection becomes even harder. 󠇟󠇠󠇡󠇢󠄙︆󠄓󠇣󠄞󠇟󠄻󠄙󠆧󠆦󠅡󠆴︄󠅙󠆋󠅾󠄥󠆨󠇧󠅧󠄣󠄹󠇥󠇇󠆮󠇞󠅪󠅼󠇁󠄖󠆩󠅘󠄋󠅅󠅥󠇭︂󠄀󠄇󠅶This means:

• Web crawlers can't filter out AI content
• Training datasets are increasingly contaminated
• The proportion of synthetic data grows invisibly

󠇟󠇠󠇡󠇢󠇜󠅧󠅾󠅦󠅁󠅵󠄶󠇝󠆔󠅳󠆭︅󠆚󠆃󠆩󠆳󠆖︈󠅴󠅫󠆊󠄮󠄌󠄕󠆤󠄱󠅣󠅇︆󠆻󠇍󠆷󠆎󠆏󠄯󠄻󠅥󠆡︌󠆧Why This Matters for AI Companies

AI companies face an existential challenge: the resource they depend on is being polluted by their own products. 󠇟󠇠󠇡󠇢󠄇󠆥󠅀󠄮󠅉󠇭󠄾󠄠󠅼󠄺󠄞󠅉󠅚󠄻󠆟󠅖󠅉󠅖󠅊󠄈󠅌󠇞󠆚󠇮󠄦󠄟󠆝󠄋󠆛︎󠇞󠄼󠅧󠆕󠇜︆󠇢󠆱󠄖󠅁### 󠇟󠇠󠇡󠇢󠆰󠅡󠄫󠅶󠄿󠇢󠄰󠄰󠅹󠇍󠆣󠅗󠄫󠇧󠆁󠄙󠅟󠇁󠅍󠆸󠄻︌︍󠄾󠄨󠄽󠅞󠅳󠇔󠄵󠅣󠇒󠆒󠆏󠄮󠄉󠄳󠆕󠄌󠄒The Training Data Crisis

The current generation of large language models was trained on data collected before AI content became widespread. 󠇟󠇠󠇡󠇢󠆘︎︃󠆗󠆭󠄓󠄰󠄷󠅳󠅔󠄐󠅰󠇥󠄧󠅼󠅽󠆿󠅺󠅭󠇑󠅕󠆽󠅧󠅜󠅷󠄘󠇊󠄾󠆋󠇞󠇟󠅳󠄕󠇤󠄇︁󠆼︂󠇍󠅳That data is:

• Finite — There's only so much pre-2022 internet content
• Already used — Major models have trained on most of it
• Not renewable — You can't create more historical human content

󠇟󠇠󠇡󠇢󠆸󠄒󠆊󠄊󠄕󠅶󠄶󠆸󠅽󠅻󠄼󠄺󠄆󠄖󠄯󠄑︇󠅲󠄛󠆏󠄭󠇆󠅉󠅃󠆝︀󠇜󠅗󠆄󠆤󠅰󠆸︃󠄸󠅚󠆫󠄢󠅴󠆌󠄷The Quality Imperative

As synthetic content pollutes future web crawls, AI companies need:

• Verified human content — Provably created by humans, not AI
• High-quality sources — Professional journalism, academic research, expert writing
• Fresh content — New human-created material, not recycled training data

󠇟󠇠󠇡󠇢󠆖󠄑󠆴󠆎󠅬󠅋󠄰︃󠆠󠆝󠄶󠅐󠅕󠆡󠅂󠅕︈󠄨󠆃󠇮󠇒︂󠆺󠅥󠄘󠆭󠅄󠆣󠄟󠄍󠅐󠆬󠇩󠆉󠄏󠅩󠄇󠄥󠇝󠄦The Competitive Advantage

AI companies that secure access to verified human content will have a significant advantage:

• Better model quality — Training on clean data produces better models
• Sustainable improvement — Continued access to human content enables ongoing training
• Differentiation — "Trained on verified human content" becomes a selling point

The Publisher Opportunity

This dynamic creates an unprecedented opportunity for publishers:

󠇟󠇠󠇡󠇢󠄊󠄞󠄰󠄫󠆡󠅐󠄷󠇓󠆓󠆝󠄳󠅊󠆫󠅽󠅘󠅀󠆱󠄉󠄗󠆈󠄞󠆘󠆬󠆻󠅇󠆌󠇐󠅃󠄜󠄻󠆚󠆚󠅢󠆵󠇏󠅕︋󠆅󠇥󠆼Your Content Is Scarce

Professional journalism, expert analysis, and quality writing are exactly what AI companies need—and can't create themselves. 󠇟󠇠󠇡󠇢󠇡︋󠅂󠄆󠆇󠆖󠄺︍󠆬󠇧󠆎󠅿󠅣󠆘󠄀󠇙󠄆󠆭󠇆󠆉󠆶󠅮󠇒󠅮󠆉︍󠆰󠇛󠆯󠆸󠆷󠆬︅󠆭󠇧󠆵︀󠄢󠆴󠆯As synthetic content floods the internet, your human-created content becomes relatively more valuable.

Provenance Enables Licensing

Content provenance solves the verification problem:

• Prove human creation — Cryptographic signatures establish content was created by your organization
• Prove publication date — Timestamps show content predates AI generation
• Enable detection — AI companies can identify your content in their pipelines
• Support licensing — Verified provenance enables enforceable licensing agreements

󠇟󠇠󠇡󠇢󠇌︂󠆤󠅹󠅕󠇥󠄿󠄝󠆠󠅞󠅌󠇦󠅉󠄟󠆨󠇭󠆗󠄗󠆵󠆍󠇍󠆱󠆽󠅚󠆆󠄉󠄒󠅍󠅫󠇮󠆆󠅳󠆿󠅆󠄞󠇬󠄋󠅗󠇦󠆄The Value Proposition

For AI companies:

"We can provide verified human-created content with cryptographic provenance. 󠇟󠇠󠇡󠇢󠆶󠄐󠆜󠆴󠄁󠇣󠄰︈󠆄󠅭󠇬󠆰󠇓󠄬󠆖󠇪󠆹󠆄󠄺󠇠󠆗󠄛︅󠇖󠅏󠅓󠅦︇󠆚󠇤󠅮󠆟󠄫󠆪󠄁󠅓󠅀󠅬󠇦󠄁You can prove to your users, regulators, and researchers that your model was trained on authentic human content—not synthetic data that degrades quality. "

󠇟󠇠󠇡󠇢󠇠️󠇖󠅑󠇞󠅧󠄽󠆧󠆡󠆬󠇬󠆁󠅶󠇎󠅎󠄐󠆽󠆖󠅷󠅐󠄙󠇔󠆉󠆭󠇆󠄴󠆨󠄯󠄹󠇝󠄈󠄊󠆍󠇎󠅝󠄵󠅲󠇗󠆼󠄹This isn't just about copyright compliance. 󠇟󠇠󠇡󠇢󠅠󠄽󠆗󠆛󠄌󠆬󠄽󠇋󠅾󠄩󠆴󠅄󠇝︆󠇤󠄨󠅷󠄺󠆶󠅤󠆟󠆗󠆇󠄘󠇃󠅨󠆓󠆎󠇒󠅌󠆅󠇒󠇏󠄣󠅄󠆮󠇎󠄉󠇣󠆒It's about model quality and competitive advantage. 󠇟󠇠󠇡󠇢󠅰︂󠅦󠄌󠆙󠇇󠄽󠆮󠆅󠄹󠄯󠄔󠄂󠅎︃󠆍󠅂󠇦󠄑󠄡󠇏󠄃󠅇︀󠆳󠅮󠅲󠇤󠄟󠆛󠅩󠅐󠄎󠇫󠅈󠆶󠄮󠆅󠇇︎## 󠇟󠇠󠇡󠇢󠆑󠄫󠄐󠄬󠅒󠄛󠄵︊󠆀󠇨󠇙󠄟︎󠆥󠇂󠄡󠆜󠇈󠇃󠅃󠆶󠇕︋󠄨󠅸󠅰󠅮󠄦󠅱󠅉󠅜󠆨󠄲󠅪󠄯󠅚︇󠄜󠅶󠄎The New Economics of Training Data

Model collapse is reshaping the economics of AI training:

Before Model Collapse Awareness

• Training data was assumed to be freely available
• Web scraping was the default acquisition method
• Data quality was a secondary concern
• Publishers were seen as adversaries to be scraped

After Model Collapse Awareness

• Verified human content is a scarce resource
• Licensing becomes the sustainable acquisition method
• Data quality is a primary competitive factor
• Publishers are partners with valuable assets

The Price of Quality 󠇟󠇠󠇡󠇢󠇊󠅧󠆛󠅾󠇑󠅽󠄿󠄄󠆖󠆛󠅟󠄚󠄰󠄾󠇁󠇞󠅈󠇡󠅳󠅎󠇢󠆵󠆳󠄧󠇜󠅝󠄗󠇔󠆴󠅊󠄦󠅸󠅀󠅍󠆨󠅇󠆰󠄇󠄌󠄪As the value of verified human content becomes clear, pricing will reflect scarcity:

Content Type	Current Value	Future Value
Unverified web scrape 󠇟󠇠󠇡󠇢󠇎󠄊󠄵󠅾󠇉󠇝󠄵󠇘󠆐󠇬󠅚󠄓󠆟︃󠅶󠆱󠅡︉󠅌󠆅󠄡󠆞󠆸󠆾󠇉󠅘󠅐󠄧󠆠󠆷󠄫󠇏󠆒󠄅󠅯󠇠󠄊󠇧󠆃󠅣	~$0	~$0 (or negative—contamination risk)
Verified human content	Emerging	Premium
Professional journalism	Undervalued	High premium
Expert/specialized content	Undervalued	Highest premium

󠇟󠇠󠇡󠇢󠆁󠆣󠇝󠄾󠄜󠅎󠄾󠆢󠆭󠄌󠅚󠆼󠇬󠆧󠄮󠅵󠆒󠇜󠆅󠇆󠇛󠆞󠆤󠆻󠇊󠆯󠅛󠆗󠇮︃󠆄󠇠󠆩󠅃󠄯󠄒󠇨󠇇󠆒󠅵What AI Companies Should Do

Short-Term Actions

1. 󠇟󠇠󠇡󠇢󠇠󠄎󠇖󠅾󠄷󠆶󠄰󠇋󠆭󠅲󠅭󠇯󠇐󠇑󠇫󠆣󠆘󠅜︍󠅵󠇁󠄟󠅻︋󠅒󠄊󠅶󠇁󠄐󠄓︀󠅕󠄡󠅺󠄲󠇡󠄩󠅺󠄋󠆣Audit training data — Understand your exposure to synthetic content
2. 󠇟󠇠󠇡󠇢󠅕󠆹󠅃󠄕󠆟󠄕󠄴󠅗󠆯󠄼󠄤︋󠄯󠅝󠄑󠅝󠅰󠄶󠅎󠇓󠄣󠅒󠄏󠆘󠅉󠅼󠅿󠆛󠅗󠆑󠇣󠄘󠄬󠆧󠆅󠅶󠆩󠇛󠆁󠅻Implement detection — Use available tools to estimate AI content proportion
3. Prioritize verified sources — Weight provenance-verified content higher
4. Engage publishers — Begin licensing conversations now

Medium-Term Strategy

1. 󠇟󠇠󠇡󠇢︇󠄑󠇋󠄘󠄓󠄐󠄱︁󠆌󠆚󠆪󠅓󠄵󠄛󠆾󠇢󠇐󠄫󠇡󠅷󠆫󠄈󠆋󠆻󠇈󠅡󠄃󠅴󠆶󠅶︆󠆠󠄀󠇠󠆛󠅉󠄂󠄣󠄊󠆄Build verification infrastructure — Integrate content provenance checking
2. 󠇟󠇠󠇡󠇢󠄆󠆏󠄯󠄋󠅢󠅅󠄼󠇏󠆆󠆑󠆠󠆤󠇐󠅥󠄃󠆗󠄫︌󠆟󠄇︅󠇇󠆮󠆇︃󠆺󠆃󠄀󠆂󠇫󠇍󠇐󠆮󠅦󠆄󠆤󠅷󠇮󠇉󠄙Establish licensing relationships — Secure access to verified human content
3. 󠇟󠇠󠇡󠇢󠄬󠅻󠄨󠄱󠄸󠄮󠄺󠆾󠆤󠄻󠇇󠆞󠇩󠆟󠇭󠅠󠆪󠅶󠆼︊󠅡︋󠆩󠄺󠄽󠅗󠅘󠇊󠅂󠄄󠆐󠄠󠄰︉󠅉󠄲󠅈󠇥󠄓󠅰Develop quality metrics — Measure and optimize for training data authenticity
4. 󠇟󠇠󠇡󠇢󠆼󠄕󠆑︇󠅄󠄱󠄰󠆀󠆚󠇜󠅕󠇍󠅩󠄳󠄚󠄍󠄧󠄌󠅶󠆳󠆛󠄕󠅳󠄢󠅃󠇊󠇗󠆛󠆗󠄼󠄣󠅓󠅊󠅍󠇍󠆔󠇑󠄝󠄘󠆼Communicate differentiation — "Trained on verified human content" as brand value

Long-Term Position

1. 󠇟󠇠󠇡󠇢󠄢󠅽︁󠅐󠆲󠄶󠄸󠆫󠆐󠅽󠄓󠆸󠄹󠄲󠅋󠆷󠆰󠆲󠅝︄󠇝󠆆󠄅󠅠󠆯󠇑󠅨︊󠇝󠇤󠆱󠆰󠅯󠄊󠇑󠅹󠄄󠇥󠄑󠅤Sustainable data pipelines — Ongoing access to fresh human content
2. 󠇟󠇠󠇡󠇢󠆺󠅴󠇙󠇎󠆴󠄨󠄻󠄤󠅶󠇩󠅶󠆹󠆍󠆁󠄭󠄒︌󠇞︆󠆇󠅕󠅱󠅶󠅸󠇮󠅎󠇔󠅹󠅚󠆒󠇢󠇨󠄐󠅿󠇌︉󠇞󠆱︉󠄃Publisher partnerships — Mutually beneficial licensing frameworks
3. 󠇟󠇠󠇡󠇢󠄋󠅥󠅳󠅉󠇯󠄃󠄶󠅮󠆑󠆠󠄴󠄼󠆀󠆎󠆌󠄺󠅺󠇇󠄁󠇒︉󠄥󠇭󠇯󠄵󠅨󠇃󠇆󠇨󠆎󠄵󠅴︎󠄩󠅦󠄢󠅤󠄯󠇧󠅑Quality leadership — Best models because of best training data
4. 󠇟󠇠󠇡󠇢󠅎󠇊󠄐󠆇󠄞󠄾󠄷󠄢󠆪󠆗󠄹󠅰󠅒󠆭︈󠆴󠄘󠇩󠆏󠇇󠅎󠆽󠇎󠆜󠇥󠆎󠆞󠄂󠄇󠇏󠅎󠆓󠆦󠅎󠇉󠅉󠆊󠄫󠅳󠄡Regulatory alignment — Proactive compliance with emerging data requirements

󠇟󠇠󠇡󠇢󠅴󠄃󠅎󠇟󠆊󠆍󠄸︍󠅴︊󠇅󠇫󠆾󠄩󠄶󠆓󠅍󠇭󠅟︃️󠄇󠆏󠅤󠄄󠆛󠄅󠆆󠅜󠆸󠅔󠆣󠄗󠄩󠅥󠆀󠄜󠆀󠆘󠄹What Publishers Should Do

Recognize Your Value

Your content is becoming more valuable, not less. 󠇟󠇠󠇡󠇢󠅽︄󠅹󠆄︀󠆡󠄴󠅂󠅰󠅛󠅳󠅋󠅥󠆾󠆳󠅨︍󠄴󠄏󠄒󠇪󠅿󠄥󠆎󠇫︅󠆻󠄖󠄏󠆽󠄟󠆝󠆍󠆁󠆖󠆑󠄤󠆾󠅑󠅩AI doesn't replace the need for human-created content—it increases it. 󠇟󠇠󠇡󠇢󠄥󠅗󠅹︁󠄔󠆩󠄻󠅳󠅷󠄍󠇬󠄺︁︅󠄪󠇭󠄂󠆄󠇈󠆍󠇘󠆖󠇑󠇀󠅨︃󠄂󠄹󠆀󠆨󠄆󠇉󠇭󠆞󠆬󠇢󠇗󠇠󠇌󠆽### Implement Provenance

Cryptographic content provenance:

• Proves your content is human-created
• Establishes publication timestamps
• Enables verification by AI companies
• Supports licensing enforcement

Prepare for Licensing

The licensing conversation is shifting:

• From: "󠇟󠇠󠇡󠇢󠅭󠆡󠆴︎󠇐󠄓󠄸󠅘󠆠󠆷󠄶󠇀󠅺︈︀󠅩󠄄󠄫󠆚󠇩󠅟󠇑󠄓󠇍︄󠄍󠆈󠆊󠄝󠆆󠆉️󠆁󠄺󠇫󠅣󠅃󠄟󠆴󠄞Pay us or we'll sue you for scraping"
• To: "󠇟󠇠󠇡󠇢󠅪󠆤󠅲󠆁󠄑󠄁󠄵󠆚󠆭󠆵󠄔󠆣󠄠󠆄󠆛󠆇󠇮󠆊󠇑󠇛󠄠󠆈󠅇󠇔󠄛󠆪󠅏󠆜︀󠇙󠇅󠆤󠄸󠅢󠆍󠅝󠄆󠆉󠄥󠆊Partner with us for verified human content that improves your models"

This is a stronger position. 󠇟󠇠󠇡󠇢󠅿󠅺󠄐󠇩󠄹󠄳󠄹󠇮󠆫󠇄󠆶󠇔󠄚󠇉󠄒󠅖󠅑󠆌󠅈󠆱󠆴󠇘󠄪󠇋󠆀󠆐󠆫󠅈󠅵󠅟︎󠇬󠄡󠄟󠅗󠅵󠅟󠆰󠄵󠇟You're not just threatening legal action—you're offering something AI companies genuinely need.

Price Appropriately As the value of verified human content becomes clear, pricing should reflect:

• Scarcity of quality human content
• Cost of model collapse to AI companies
• Competitive advantage of verified training data
• Ongoing value of fresh content access

The Convergence

Model collapse creates a convergence of interests:

*Publishers want: *

• Fair compensation for content use
• Protection of intellectual property
• Sustainable business models

AI companies want:

• High-quality training data
• Verified human content
• Sustainable data pipelines
• Regulatory compliance

Content provenance enables:

• Verification of human creation
• Licensing infrastructure
• Quality differentiation
• Mutual benefit

This isn't a zero-sum conflict. 󠇟󠇠󠇡󠇢󠄠󠅧󠄭󠅬󠆭󠄅󠄷󠅴󠆥󠅂󠅀󠄌󠇮󠄙󠇉󠄲󠄫︈︇󠆽󠅎󠅱󠅭󠆉︄󠄀󠆦󠆸󠅹󠆜󠅁󠇣󠇃󠄸󠆹󠄔󠄾󠆮󠆥󠆆It's an opportunity for partnership built on verified content provenance. 󠇟󠇠󠇡󠇢󠆅󠅮󠆦󠄿󠄚󠆎󠄿󠇢󠆯󠅝󠄣󠄭󠅜󠄁󠄱󠅰󠄐󠄞󠅹󠅐︍󠇭︆󠆁󠅆󠆍󠄓󠅢󠄔󠆇󠆿︄️󠆏︈󠄏︉󠆨󠄖︉## 󠇟󠇠󠇡󠇢󠆝󠄴󠄊󠅞󠆼󠄧󠄻󠄭󠅲󠆗󠆵󠆬󠆢󠅼︄󠆲󠆪󠇔󠇬󠄾󠄼󠇝󠇢󠆛󠄪︅󠄁󠅿󠄁󠇐󠄠󠅯󠇏󠅨󠆝󠄷󠇦󠅥󠅖󠄇The Timeline

2025-2026: 󠇟󠇠󠇡󠇢󠅃󠇨󠆩󠅵󠅵󠆀󠄴︁󠆮󠆱󠅽󠇚󠅎󠄥󠅀󠅊󠅔󠇘󠅣󠄐󠄴󠆣︂󠆕󠅩󠆚󠅄󠆏󠅹󠅘󠆴︀︂󠄊󠅇󠄙󠅔󠆉󠆲󠅅Awareness Phase

• Research on model collapse gains mainstream attention
• AI companies begin auditing training data quality
• Early licensing deals for verified content emerge
• Provenance infrastructure develops

2026-2027: 󠇟󠇠󠇡󠇢󠅤󠇅󠇋󠅺󠅑󠅶󠄹󠄠󠆬󠄬󠇩󠄯󠄅󠆞󠅹󠅁󠆒󠄒󠄇󠆋󠅏󠅍󠄧󠄓󠇏󠄬󠄾󠆰󠄴󠅸󠄇󠆔󠄯󠅈󠆦︅󠆪󠆋︆󠆠Transition Phase

• Verified human content commands premium pricing
• Major AI companies establish publisher partnerships
• "Trained on verified content" becomes marketing differentiator
• Unverified web scraping becomes liability

2027+: New Equilibrium

• Licensing is standard for quality training data
• Content provenance is infrastructure
• Model quality correlates with training data verification
• Publishers and AI companies operate as partners

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Conclusion

Model collapse isn't a distant theoretical concern—it's an emerging reality that will reshape the AI industry. 󠇟󠇠󠇡󠇢󠄊󠅈󠇊󠄭󠅀︍󠄽󠇏󠆃󠄋󠅫󠅐󠄺󠅄󠅯︀󠅂󠄵󠅘󠄖󠄕󠅤󠇉︎󠄎󠇌󠄇󠄺󠆯󠇉󠅾󠄺󠇯󠇥󠄄󠆼󠅊󠅔󠅰󠄆As synthetic content floods the internet, verified human-created content becomes the scarcest and most valuable resource in AI. 󠇟󠇠󠇡󠇢󠅳󠄱󠅲󠆐󠆸󠆵󠄶󠅯󠅺󠅫󠅜󠇠󠆎󠆢󠄽󠇍󠄞󠆼󠅦󠇗󠄸󠆏󠄖󠄲󠄻󠅒󠅋󠆮󠇂󠄷󠅾󠅾󠆻󠇆󠆦󠇒󠄕󠅢󠄤󠆦For publishers, this represents a fundamental shift in leverage. 󠇟󠇠󠇡󠇢󠇯󠇨󠄐󠆪󠆏󠄃󠄳󠅭󠅾󠄱󠄶󠄲󠆶󠅧󠅱󠆟󠅡󠇩󠆭󠇒󠄛󠆉󠆰󠄚󠅵󠅷󠆅󠇉󠅍󠄓󠅰󠇁󠄤󠇑󠆢󠇨󠄀󠆙󠆃󠄐Your content isn't just intellectual property to be protected—it's a critical resource that AI companies need to maintain model quality. 󠇟󠇠󠇡󠇢󠄚󠄭󠄰󠇦󠆖󠇇󠄸󠄢󠆨󠄄󠅜󠅆󠆧󠅘󠆴󠅘󠄀󠄄󠆯󠆞󠄯󠆓󠆆󠇍󠇓󠇧󠄤󠆉󠆙󠄇󠇬󠄌󠄜󠇉󠆳︊󠅠󠆊󠅘󠇄Content provenance is the key that unlocks this value. 󠇟󠇠󠇡󠇢󠅟󠄏󠇙󠆲󠇤󠆡󠄿󠆈󠅶󠆌󠇎󠇮󠄒󠅐󠇦󠆿󠇨󠄀󠇦󠅳󠆶︎󠄬󠆣󠄥󠆑󠅣󠅍󠆚󠇊󠅸󠇊󠇫󠅬󠆽󠇭󠅴󠆵󠆩󠅵It enables verification, supports licensing, and creates the infrastructure for sustainable partnership between publishers and AI companies. 󠇟󠇠󠇡󠇢󠄙󠅖󠅪󠅝󠇂󠄌󠄺󠄥󠅺󠅶󠆑󠆝󠅡󠅤󠆈󠄍󠆩󠆑󠇚󠇄󠄙󠅆󠄣󠄂󠄄󠅆󠆼󠇕󠅯󠆗︉󠆻󠅚󠄜󠄂󠄨󠄵󠆖󠇕︊The question isn't whether this transition will happen. 󠇟󠇠󠇡󠇢󠆬󠄩󠅰󠄰󠄕󠇤󠄺󠅑󠆕󠄷󠆱󠅓󠇜󠄲󠆻󠄖󠅃󠄔󠅋︈󠇂󠇢󠄗󠆽󠅕󠅯󠅅󠅌󠄝󠆟󠇞󠅜󠆆󠅽󠄮󠅉󠇁󠆃󠄽󠄢It's whether you'll be positioned to benefit from it. 󠇟󠇠󠇡󠇢󠄡󠆅󠇫󠆺󠆒󠇗󠄼󠄄󠆋󠄓󠅜︉󠄰󠄆󠅐󠅂󠄓󠇓󠇤󠆣󠇉󠆼󠄢󠄭󠅽︄︋󠅼󠆄󠅠󠅘󠄳󠄃󠄟󠄈󠅀󠄳󠄩󠄻󠄘Learn more about content provenance for the AI era: 󠇟󠇠󠇡󠇢󠅐󠄖󠆥󠄉󠅤󠇮󠄰󠄦󠆦󠆥󠅞󠆙󠆐󠇕󠅅󠆛󠄙󠅮󠄙󠄤󠅣󠇃󠅌󠇑󠆎󠆵󠇕󠄟󠅟󠆉󠄻󠇟󠆇󠇍󠆫󠅴󠄺󠄐󠅸󠅚encypherai.com

#ModelCollapse #AITraining #DataQuality #SyntheticData #ContentProvenance󠇟󠇠󠇡󠇢󠆽󠇥󠄹󠆤󠅨󠄩󠄸󠆏󠆚󠄚󠇕󠄩︈󠇟󠅶󠆉󠆭󠅥󠅐󠆥󠇐󠇏󠄌󠄺󠄙︀󠆨󠆈󠄍󠆷︆󠇇󠆯󠅚󠆂󠄎󠅆󠇍󠄉󠄺