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Copyright Laws + AI: Should You Be Concerned?
Dr. Ryan Ries here. We haven’t talked about copyright laws and AI in a while, and I think with the news recently, we have some things to discuss.
Copyright Wars
So much has happened in just the last few days around the copyright topic.
Anthropic quietly settled its class action lawsuit with a group of authors, making this the first major AI copyright settlement in the U.S.
The settlement came just months before a trial that could have resulted in catastrophic damages. With statutory penalties of up to $150,000 per pirated work and 7 million allegedly infringed books, Anthropic was staring down potential liability in the hundreds of billions.
The fact that they chose to settle rather than fight tells us they recognized the weakness of their position on the piracy claims, even though the court had ruled in their favor on the fair use training question.
Meanwhile, Japanese media giants Nikkei and Asahi are demanding $15 million each from Perplexity for allegedly scraping their content.
I recently had a customer say they're afraid to use certain AI models because of potential copyright issues, and I get it! We all need to pay close attention to this.
Potential Roadmap for AI Copyright Law
The recent Anthropic case gives us the clearest picture yet of where courts are heading. Judge William Alsup made a crucial distinction that every enterprise needs to understand:
Training AI models on legally obtained copyrighted material = Fair use.
Downloading millions of pirated books from shady internet sites = That's going to cost you.
This bifurcated decision can give us a sort of roadmap. The judge essentially said, "If you bought it or legally accessed it, you can probably train on it. If you pirated it, prepare to pay statutory damages of up to $150,000 per work."
In the Anthropic case, it says they allegedly pirated 7 million books… You can do the math on that potential liability.
The Real Business Problem
While all of this is certainly nerve-wracking, it’s important not to make technology decisions based on fear instead of understanding.
At the end of the day, it isn’t as scary as the headlines make it seem.
What matters is:
- Where did your training data come from? If you're using models trained on properly licensed or legally obtained content, your risk profile is dramatically different than models trained on pirated materials.
- What’s your use case? The transformative nature of how you're using the AI matters legally. Training models to create entirely new content has stronger fair use protection than reproducing existing works.
- What model are you using? Major AI providers are increasingly taking on indemnification responsibilities. AWS Bedrock, for example, provides certain protections around its foundation models.
Building Copyright-Conscious AI Strategies
Here's what I'm telling customers who ask about this:
First, understand your models' training data lineage. Responsible AI providers should be able to tell you about their data sources and legal frameworks.
Second, implement content filtering and attribution systems. If your AI outputs might contain copyrighted elements, build detection and attribution into your workflow.
Third, consider synthetic and licensed data alternatives. More companies are creating their own training datasets or licensing content specifically for AI training.
Fourth, work with legal teams early. Don't let fear of copyright issues paralyze your AI strategy, but don't ignore them either.
What's Next
Copyright law is still evolving around AI, but the direction is becoming clearer.
Companies that obtain content legally and use it transformatively have strong fair use protections. Companies that cut corners on content licensing face significant liability.
We help customers navigate exactly these kinds of challenges. If you're building AI systems and want to discuss copyright-conscious architecture, I'd love to chat about your specific use case. As always, feel free to reach out.
Until next time,
Ryan
Now, time for our AI-generated image and the prompt I used to create it.
(AI-generated LEGO photo)
(Reference Photo)
Author Spotlight:
Ryan Ries
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