Anyone else worried about content rights with AI? The legal landscape is getting wild

IP attorney here who’s been following these cases closely.

The legal landscape is shifting fast:

The Anthropic $1.5 billion settlement (Bartz v. Anthropic) is the largest copyright recovery in U.S. history. It compensated roughly 500,000 works at about $3,000 per work. That’s not huge per piece, but it signals that courts will impose real penalties.

What the Copyright Office is saying:

The May 2025 report was significant. They concluded that:

• Using copyrighted works to train AI may constitute infringement
• Fair use doesn’t automatically apply to AI training
• The key question is whether AI outputs compete with original works

For individual creators:

You have more leverage than you think. Document everything - when you published, what platforms used your content. Consider joining collective licensing organizations that are forming specifically for AI rights.

The German court ruling against OpenAI for training on licensed music content shows this isn’t just a U.S. issue. International pressure is mounting.

We faced this exact dilemma at our company.

The blocking vs. visibility tradeoff is real:

Initially we blocked GPTBot and ClaudeBot. Traffic stayed stable for about 3 months, then we noticed something concerning - our brand mentions in AI responses dropped significantly. Competitors who allowed crawling were getting cited instead.

What we do now:

1. Allow AI crawlers but implement clear licensing terms in robots.txt
2. Use Am I Cited to monitor when and how we’re being cited
3. Track whether citations drive referral traffic
4. Document everything for potential future claims

The monitoring is crucial. You can’t protect what you can’t see. We track our citation rates weekly across ChatGPT, Perplexity, and Claude.

The reality is that blocking AI might hurt your visibility more than it protects your content, since training already happened on historical data.

Large publisher perspective here.

We’re in active licensing negotiations with multiple AI companies. What I can share:

What moves the needle in negotiations:

• Traffic volume and authority metrics
• Unique, proprietary content that can’t be replicated
• Clear documentation of original publication dates
• Willingness to walk away (blocking really)

The collective licensing approach:

We’re working with industry groups to form collective organizations similar to ASCAP for music. The challenge is that content is so diverse - news, tutorials, creative writing all have different value propositions.

My honest take:

Individual creators will struggle to negotiate directly. The power imbalance is too great. Collective action is the path forward.

The EU’s approach with opt-out registries is promising. Shifting the burden to AI companies to prove permission rather than creators proving infringement would be huge.

This is all really helpful context.

The collective licensing idea makes sense - I’m definitely going to look into organizations that represent independent creators.

Follow-up question: For those using monitoring tools - are you seeing AI systems actually attribute sources correctly, or is it mostly unattributed generation?

The difference matters a lot for whether visibility in AI responses is actually valuable.

Great question. I’ve been studying citation patterns across AI platforms.

Attribution varies wildly by platform:

• Perplexity: Best at citing sources - almost always provides links
• ChatGPT with browsing: Cites when actively searching, not for training-based responses
• Claude: Improving but inconsistent
• Google AI Overview: Links to source pages when included

The key distinction:

There’s a difference between:

1. Training on your content (no attribution, historical)
2. Retrieval/RAG systems citing your content (attribution, real-time)

RAG systems are actually good for creators - they drive traffic and provide attribution. The training issue is the problem, and that’s where the legal battles are focused.

This is why monitoring matters. You want to see if you’re being cited in retrieval-based responses, because those are the ones that benefit you.

Smaller creator here with a practical approach.

I stopped fighting it and started optimizing for it. If AI systems are going to use my content anyway, I want to:

1. Be cited rather than summarized - Format content for easy attribution
2. Track mentions - Know when I’m referenced
3. Build authority - More authority = more citations = more traffic

What’s actually working:

• Question-answer format in my content
• Clear expert credentials in author bios
• Regular monitoring of AI responses for my topics

It’s not ideal that we’re adapting to a system that took without asking. But pragmatically, visibility in AI responses is becoming as important as SEO.

The Glaze-type protection tools are interesting but I worry they’ll become an arms race. Better to be visible and documented than hidden and forgotten.

Adding a few practical legal steps for creators:

Document everything:

• Screenshots of your original publication with dates
• Archive.org captures for historical proof
• Any evidence of AI systems reproducing your content

Register your copyrights: In the U.S., you can’t sue for infringement without registration. The Copyright Office has been slow but it’s essential for any future claims.

Watch the class actions: Several are forming for different content types. Even if you don’t lead, you may be able to join as a class member.

Read the guardrails guidance: The Copyright Office noted that AI companies implementing content filtering and similarity prevention may have stronger fair use arguments. This creates pressure on platforms to respect creator rights.

The legal framework is being built right now. Creators who document and organize will be positioned to benefit.

Agency perspective managing content for dozens of clients.

The hybrid approach we recommend:

1. Allow AI crawlers for discovery and citation benefits
2. Monitor aggressively with tools like Am I Cited
3. Clear licensing terms in legal pages
4. Document everything for potential future claims

What we’re telling clients:

The horse has left the barn on historical training data. Fighting that is expensive and uncertain. Focus on:

• Being cited in current RAG/retrieval systems (drives traffic)
• Building the documentation trail for future compensation frameworks
• Participating in collective licensing efforts

The China and EU approaches are interesting - both provide more creator protection than U.S. currently does. We’re watching for regulatory changes that might shift the balance.

Running a small indie publication with a team of 5 writers.

Our reality check:

We can’t afford to litigate against OpenAI or Anthropic. We can’t negotiate licensing deals individually. But we can:

• Track our visibility in AI responses
• Optimize content for citation
• Document everything
• Join collective efforts when they emerge

The monitoring insight:

Started using Am I Cited 3 months ago. Discovered we were being cited fairly often in our niche - but competitors were cited more despite having less authoritative content. That gap is what we’re now working to close.

The content rights issue isn’t going away, but neither is AI search. We’re playing both games - advocating for better creator rights while optimizing for the current reality.

This thread has been incredibly valuable. Thank you all.

My takeaways:

1. The legal landscape is shifting - major settlements and court decisions are establishing that AI training isn’t automatic fair use
2. Collective action is key - individual creators need to join forces through licensing organizations
3. Monitor and document - track AI citations now to benefit from future compensation frameworks
4. Pragmatic optimization - while fighting for rights, also optimize for visibility in current systems
5. Distinguish training from retrieval - RAG-based citations actually benefit creators

My next steps:

• Start monitoring with Am I Cited
• Look into collective licensing organizations
• Document my content with clear publication dates
• Keep creating quality content that AI systems want to cite

The music industry parallel is apt - creators eventually got streaming royalties, but only after years of organizing. We need to do the same.