In the midst of the AI glory days, in a room buzzing with the impact and momentum of the industry, no one seemed to care about copyrights except the speaker on the Disrupt stage.
While watching Martin Casado of A16Z deftly handle questions about AI regulation – he stopped me by noting the importance of copyright to regulation.
The room packed with the usual entrepreneurs, investors, and journalists eager to hear how Silicon Valley will lead the AI revolution. But something wasn't sitting right with me.
"Why do tech companies treat AI training like a copyright-free zone?"
I finally asked, cutting through the careful corporate speak.
The room went quiet. Casado's response was telling - he immediately reached for the comfortable analogy of Napster and how peer-to-peer sharing eventually led to Spotify and Apple Music.
As someone who lived through that transformation, I knew the punchline he was missing: musicians now earn a fraction of what they once did.
We're about to repeat history, but this time it's not just music - it's every form of human creativity.
Copyright is The Castle, AI has Wings
Copyright law is like a rundown medieval castle. For centuries, it protected creative works with its stone walls, drawbridges, and vigilant guards.
Then AI showed up - not with battering rams, but with wings. Our carefully constructed walls became meaningless overnight.
"The old rules weren't built for machines that can process centuries of human creativity before breakfast,"
"We're using rules written for printing presses to regulate digital shapeshifters."
What if one day a talented artist receives a cease-and-desist letter for AI-generated art that infringes on her own style.
It’s possible to be legally challenged over AI mimicking her creativity, while the same AI had likely trained on her work without permission or compensation.
The Breaking Point Is Here
The collision between old laws and new technology isn't theoretical - it's happening right now. At TechCrunch Disrupt, I watched this play out in real time.
When tech leaders discuss AI training, they treat copyrighted work like it's a free-for-all resource.
But for creators watching their life's work being ingested into AI models without compensation or consent, it's not so simple.
This is where we hit two colliding truths:
Engineers say: "Input isn't copyrightable"
Creators say: "That's my life's work"
Both are right. Both are wrong. And that's exactly why we need a new approach.
Beyond Protection: The Participation Economy
Copyright law was built for a world of physical delivery - books, records, paintings you could hold in your hands.
It was about protecting specific copies from unauthorized duplication.
But in today's digital age, where AI can process and transform millions of works instantly, that framework isn't just outdated - it's obsolete.
The solution isn't more lawsuits or stronger copyright walls. It's about building a new system that benefits both creators and AI development.
LLMs need quality training data to build better language models.
Creators need fair compensation and recognition for their work.
Instead of seeing these as competing interests, we can align them.
I've developed two potential frameworks to help creators and AI interact. Not through courts and litigation, but through code and collaboration.
These solutions focus on measuring and rewarding actual influence - how much a creator's work contributes to AI outputs and model improvement.
The Token Economy: Rethinking Creative Value
Thinking about the comparison between today's AI to the Napster era, it crystallized something for me.
We're not just facing a copyright problem - we're facing a value recognition problem.
But unlike the music industry's painful transition to streaming, we have a chance to build something better from the start.
The Token Economy model I'm proposing treats creative works like living assets rather than static files. Here's how it works:
Influence Tracking in Action
Imagine each piece of creative content broken down into tokens - not just words or images, but meaningful building blocks that carry their influence forward.
Every time an AI model learns from or uses these tokens, it creates a trackable imprint.
This isn't theoretical - we're already seeing similar principles at work in how transformer models process and weight different inputs.
Smart Compensation That Scales
Let's talk real numbers:
Training an AI model costs roughly millions to hundreds of millions:
Current licensing deals max out around $20-100 million total for larger businesses
Most creators earn pennies per view/use of their content
Instead of flat licensing fees or per-use payments, make compensation dynamic:
High-impact content (like authoritative research papers) earns premium compensation
Generic content (like basic product descriptions) receives minimal compensation
Unique creative works (like distinctive writing styles or innovative code) earn mid-tier compensation
The Efficiency Breakthrough
This model doesn't just serve creators - it makes AI development more efficient. Here's why:
Better training data leads to better models
Transparent compensation reduces legal risks and costs
Standardized systems lower content acquisition expenses
What if compensation was based on actual influence and value created?
The metric would serve the business and a select group of creators would be rewarded financially, others with credits and bonuses.
Improving AI Training with Fair Participation
When we talk about LLMs and training data, quality matters more than quantity. Right now, AI companies are scraping everything they can find, treating all content as equal.
But we know that's not true. Some content - whether through its clarity, originality, or influence - contributes more to model performance than others.
Performance-Based Value Creation
Here's what happens when we shift from protection to participation:
Value Tiers That Make Sense:
Research papers that improve model accuracy
Creative works that enhance output quality
Technical content that improves specialized knowledge
Cultural works that help models understand context
Each tier gets compensated based on measurable improvements in model performance. It's not just about paying for content - it's about investing in quality.
The Technical Framework
I've looked at a proof-of-concept system that tracks how AI models pay attention when they create.
Think of it like a heart rate monitor for creativity. Every time the AI focuses on certain parts of its training, we can trace those patterns. This isn't perfect attribution, but it's measurable impact.
The code does something fascinating:
Watches attention patterns across neural networks
Identifies distinctive signatures in content influence
Creates trackable metrics for compensation
Download the Code
Download the Claude discussion PDF
Real-World Applications
We're already seeing similar ideas emerge:
Adobe's Content Credentials system tracking image origins
Credtent empowering creators to control how LLMs use their work
Digital Influence Rights: Beyond Traditional Copyright
The challenge with traditional copyright isn't just technological - it's conceptual.
We're trying to apply rules meant for copying books to systems that transform and recombine information in entirely new ways.
Instead of fighting this transformation, we need to embrace and shape it.
What Are Digital Influence Rights?
Digital influence rights represent a fundamental shift in how we think about creative value in the AI era. Instead of focusing on preventing copying, these rights center on:
Measuring how content shapes AI outputs
Tracking the impact of creative work on model performance
Creating value through influence rather than restriction
Core Mechanics
Value-Based Tiers
High-impact content earns premium rights
Cultural significance affects earnings
Dynamic pricing based on actual use and influence in improving AI outputs
Creator Benefits Program
AI system credits that grow with contribution
Premium access rights to AI tools
Direct influence over future AI training decisions
Democratic Access
Small creators can participate without legal overhead
Impact-based earnings tracked through code
Technical solutions replace legal battles
Technical Implementation
The system works by:
Tracking attention patterns in neural networks
Measuring how different inputs affect model performance
Creating transparent metrics for value distribution
While the code for these systems is still emerging, we're seeing similar concepts being developed across the industry.
For those interested in learning more about these developing technologies, I recommend following:
· Technical discussions about AI attribution systems
· Open source AI model development communities
· Creator rights organizations working on AI attribution
· Companies developing content credentialing systems
Tracing AI's Creative DNA: Starting the Conversation
When I talk about tracking creative influence in AI, some say it's impossible - like trying to find a specific drop of water after it's been poured into the ocean.
They're not wrong about the complexity, but they're missing an important point.
We don't need perfect attribution to start building better systems.
Netflix doesn't know exactly why you watched that show, but they can make educated guesses about what influences your choices.
Spotify doesn't perfectly understand music but can track patterns of influence and similarity well enough to build a business model.
I'm not an engineer, but I've been exploring these concepts with AI tools to understand the possibilities.
The proof-of-concept ideas I'm working on are simple starting points - ways to begin thinking about how we might track and value creative influence in AI systems.
It's like watching the attention patterns of AI models - where do they focus? What influences their outputs?
This isn't about building a perfect system. It's about starting a conversation and exploring practical first steps toward fair attribution and compensation.
The New Creator Economy: Participation Over Protection
We're not just facing a copyright crisis - we're witnessing the birth of a new creative economy.
One where the question isn't whether AI will use your work, but how you'll participate in shaping its development.
When an AI model ingests creative work, that influence disappears into a black box.
Creators lose connection to their work's impact, while AI companies gain value without sharing it. But it doesn't have to be this way.
Instead of focusing on restricting AI's use of creative work, we need to build systems that:
Track real influence over AI outputs
Reward meaningful contributions to model improvement
Create new opportunities for creator participation
This isn't about stopping AI - it's about making it work better for everyone.
When creators see and benefit from their influence on AI systems, they're more likely to contribute quality work. When AI companies measure and reward valuable contributions, they build better models.
By measuring influence and creating clear value pathways, we can build a creative economy that works in the age of AI.
Building Tomorrow's Rules: Performance Over Protection
Traditional copyright law looks increasingly like a relic - rules written for printing presses trying to govern quantum computers.
The solution isn't more restrictive laws. It's smarter systems.
The core challenge is clear. Current copyright frameworks focus on controlling copies, but AI doesn't just copy - it learns, transforms, and generates.
We need rules built for this new reality. Rules that focus on:
Measuring real impact rather than restricting access
Rewarding performance instead of policing permission
Creating value through participation, not protection
We're seeing the first signs of this transformation in how AI companies approach training data.
The question isn't whether they'll use creative work - it's how to make that use fair and beneficial for everyone involved.
The future won't be built in courtrooms. It will emerge from new systems that:
Track and reward creative influence
Enable small creators to participate without legal battles
Allow innovation and fairness to coexist through smart technology
The question isn't if we'll move beyond traditional copyright - it's how quickly we'll build something better.
AI for Creators and Developers - A Model for Discussion
Opening New Possibilities: Beyond Copyright Litigation
"Amazing stuff doesn't come from the middle soup that generative AI will create."
This is crucial to understand - we're not just talking about compensation, we're talking about finding those edge cases that create extraordinary results.
Let me be clear about where we stand:
"We don't need better copyright laws.
We need a new system that turns creators from adversaries into stakeholders."
The question isn't whether AI will use your work - it's whether you'll help shape how this happens.
Here's what's possible right now:
For Creators:
"You won't immediately get paid for AI using your work, but you will have a way of understanding how it does."
This transparency is the first step toward fair participation.
For Engineers:
Yes, we recognize the limitations of attention-based attribution. But that's exactly why we need your help to:
"make something better because we could do things here that could really help, not only help creators compensate them, but also find those edge cases that make the amazing stuff."
A System of Aligned Incentives:
Instead of random licensing fees, imagine a system where:
"Usage influence tracking" quantifies how creator input affects specific outputs
Higher influence leads to higher credits, creating relevance-based compensation
"Think of this as intellectual currency" - creators don't lose control, they gain influence
"This isn't about lawsuits. It's a tech-enabled system where content usage aligns with incentives for creators."
Building Future to Cooperate instead of compete:
Value-based tiers recognizing different levels of impact
An AI-backed loyalty program offering both monetary and non-monetary rewards
Leveling the playing field for smaller creators through collective impact
We're witnessing the birth of something new - a system where creators and AI can grow together rather than fight each other.
Instead of wasting resources on copyright litigation - which requires deep pockets and armies of lawyers - we can build technology that aligns incentives, tracks influence, and rewards meaningful contributions.
The future isn't about protecting content from AI; it's about participating in how AI evolves.
Whether you're a creator wondering about your AI influence or an engineer interested in these technical challenges, there's a role for you in shaping this future.
The tools are emerging. The possibilities are clear.
Now it's time to move beyond the copyright battles of yesterday and build something better together - a system that turns creators from adversaries into stakeholders in AI.
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