By Gregory A. Catsaros
CEO, The Magazine Coalition
AI companies aren’t licensing articles. They’re acquiring structured datasets, at scale, with clarity, and with reliability.
AI licensing discussions often start from the publisher’s perspective: ownership, protection, and monetization.
But that’s only part of the picture.
To understand how this market is forming and why progress has been limited, you have to look at it from the other side.
What do AI companies actually need when they acquire content?
A Shift in Perspective
AI platforms are not licensing individual articles, images, or videos one at a time.
They are building systems that depend on large, diverse, and continuously updated inputs.
In practice, they are not buying content in pieces.
They are acquiring datasets, large, structured collections of content.
What “Dataset Scale” Actually Means
Dataset scale goes beyond volume. It includes several requirements working together:
- Volume
Millions of data points, not thousands. - Diversity
Coverage across topics, formats, timeframes, and perspectives. - Consistency
Standardized formatting, metadata, and structure. - Structured Access
Programmatic delivery through APIs, feeds, or pipelines so content can be accessed automatically, not manually transferred.
This is different from how most publishing content has traditionally been bought and sold.
What Buyers Actually Need
Buyer requirements are straightforward. AI platforms are looking for:
- Scale
Enough content to meaningfully train or improve models. - Clarity of Rights
Confidence that content can be used as intended, without ambiguity or downstream risk. - Efficiency
Low-friction transactions. Managing thousands of individual agreements is not practical. - Reliability
Ongoing, predictable access to updated content.
These are baseline requirements, not edge cases.
Where the Market Breaks
This is where the publishing ecosystem starts to break down:
- Fragmented Rights
Rights are distributed across thousands of publishers, often with inconsistent terms. - Inconsistent Ownership
Contributor agreements and legacy contracts can create uncertainty around what is licensable. - Lack of Aggregation
Individual publishers rarely have the scale or breadth required on their own. - No Standardized Access
Content is not consistently packaged or delivered in ways that align with AI system needs.
The result is a mismatch between how buyers operate and how supply is structured.
Buyers require structured, reliable datasets at scale.
Supply exists, but in fragmented, inconsistent, and difficult-to-access forms.
The Implication
This helps explain why progress in AI licensing has been limited.
It’s not only a question of willingness to pay or enforce rights.
It’s a question of market structure.
Most individual publishers are not positioned to meet dataset-scale requirements directly. Not because the content lacks value, but because it lacks coordination.
This is where aggregation begins to matter.
As outlined in The Network Advantage: How Early Rights Networks Shape AI Licensing Markets, early coordination begins to align fragmented supply with how buyers actually transact.
What Comes Next
If aggregation brings supply together, the next challenge is making that supply usable.
That requires more than volume.
It requires a way to verify, standardize, and authorize content at scale. A way to confirm rights and manage usage consistently across large datasets.
That is where an authorization layer begins to emerge, and why it becomes critical to the next phase of the market.
About the Author
Gregory Catsaros is CEO of The Magazine Coalition, an initiative advancing copyright enforcement and collective licensing in the AI era. With decades of experience in media and publishing, Gregory’s work centers on helping publishers organize their rights and content to support enforcement and emerging AI licensing markets.
If you’re exploring how content, rights, and AI systems intersect, request a partnership discussion to continue the conversation.
