Has anyone else noticed how much AI platforms rely on news publishers? The licensing drama is getting crazy

I’m on the front lines of this at a major publication. Let me share what’s really happening behind the scenes.

The licensing negotiations are brutal:

• AI companies initially wanted everything for free
• Publishers had to threaten lawsuits to get any compensation
• Even the “big deals” are tiny compared to what these companies make

What I’ve seen change:

• We now have a team dedicated to AI visibility optimization
• Our content strategy explicitly considers “how will AI summarize this?”
• We track which articles get cited in ChatGPT responses

The 60% error rate in AI-generated content that Tow Center found? That’s real. I’ve seen our reporting misattributed, misquoted, and sometimes completely fabricated with our name attached.

We use Am I Cited to monitor this now. Without tracking tools, you’re blind to how your content is being used.

Let me add some academic perspective here.

The fundamental problem:

News publishers created billions of dollars worth of training data. AI companies used it. Now those same AI companies are disrupting the publishers’ business model.

The data tells the story:

• AI bots increased Wikipedia bandwidth by 50% since January 2024
• Most expensive requests (65%) come from AI crawlers
• Publishers report 15-30% traffic drops from AI summaries

What needs to happen:

1. Mandatory attribution standards (like CC-BY-SA requires)
2. Revenue sharing for training data usage
3. Real-time citation tracking infrastructure

The Wikimedia Foundation is pushing back. News publishers should too. The “AI cannot exist without human-created content” argument is literally true.

Coming from the SEO world, I’ve had to completely rethink how we approach news content.

The old playbook:

• Optimize for Google rankings
• Get featured snippets
• Drive traffic through search

The new reality:

• Optimize for AI citations
• Get included in AI summaries
• Visibility matters even without clicks

Here’s what actually works for news content in AI:

1. Clear, structured headlines - AI systems parse these easily
2. Inverted pyramid style - Key info first, AI heavily weights first 50 words
3. Expert quotes with attribution - AI loves citable sources
4. Factual, verifiable claims - AI systems cross-reference

The publishers winning aren’t fighting AI - they’re adapting their content strategy.

From the PR side, this changes everything about media relations.

Old goal: Get coverage in major outlets for brand credibility

New goal: Get coverage that AI will cite when people ask about our industry

I’m now asking different questions before pitching:

• Does this publication get cited by AI platforms?
• Will this story structure work for AI summarization?
• Is the journalist’s byline authoritative in this space?

We’ve started tracking press coverage appearances in AI responses using monitoring tools. It’s eye-opening - sometimes smaller industry publications get more AI citations than major outlets.

The ROI calculation for PR has fundamentally changed.

Running content strategy for a B2B publication. Here’s our data:

What gets cited in AI (from our analysis):

• Comprehensive industry reports: 45% citation rate
• Breaking news with original reporting: 38%
• Expert commentary and analysis: 32%
• Generic news rewrites: 8%

The pattern is clear - original reporting wins. AI systems seem to detect and prefer primary sources over syndicated content.

We’ve restructured our editorial calendar around this:

• More original research
• Deeper expert interviews
• Exclusive data and surveys

The bonus? This content also performs better for traditional SEO and reader engagement.

I cover AI for a living and the irony isn’t lost on me.

The publishers’ dilemma:

• Block AI crawlers = lose visibility in AI search
• Allow AI crawlers = content gets used without compensation
• Sue AI companies = expensive and uncertain outcome

What Perplexity’s Publisher Program actually does:

• RAG technology includes publisher content in real-time
• Revenue sharing when content is cited
• Attribution with direct links

This is the model that could work. But not all AI platforms are playing nice.

I’ve been using Am I Cited to track my own bylines across AI platforms. Found my articles cited dozens of times last month - mostly without clear attribution. That’s the problem we need to solve.

We implemented a full AI visibility strategy last year. Here’s our playbook:

Technical changes:

• Schema markup on all articles (Article, NewsArticle types)
• Proper author attribution and byline pages
• Clear publication dates and update timestamps

Editorial changes:

• “AI summary first paragraph” - key info in first 50 words
• More FAQ sections addressing common questions
• Explicit factual claims with citations

Results after 6 months:

• 40% increase in AI platform citations
• Slight decrease in direct traffic (expected)
• Higher engagement from traffic we do get

The traffic quality matters. Users who click through from AI summaries already know what they want - conversion rates are higher.

This thread is exactly what I needed. Key takeaways:

For publishers:

1. Original reporting > syndicated content for AI citations
2. Structure content for AI parsing (first 50 words matter)
3. Track your AI visibility - you can’t improve what you can’t measure
4. Smaller outlets can win on local/niche authority

For brands working with publishers:

1. Choose publications that get cited by AI platforms
2. Story structure matters for AI summarization
3. Expert quotes and attribution increase citation likelihood

My action items:

• Set up Am I Cited monitoring for our publications
• Audit our content structure against AI best practices
• Start tracking citation patterns across ChatGPT, Perplexity, and Google AI

The licensing deals are table stakes. The real competition is who gets cited in AI responses.