“AI’s” Legal Win Shouldn’t Blind Us to the Bigger Fight

Christopher J Kouzios

A federal judge just ruled that Anthropic’s use of purchased books to train its AI models is legal. According to the court, if a human can read and learn from a book, so can a machine. The logic? Learning is learning.

But that comparison is way too clean and way too convenient. In the long run, it might also be too dangerous.

Learning or Extracting?

Let’s not kid ourselves. A human reads a book, forms opinions, forgets parts, and learns in context. I have a friend who reviews every book he reads on LinkedIn…and he reads a lot. Dozens of books a year. He’s sharp, but there’s no way he retains every word of every page. He absorbs the key messages and a few critical elements. Absorb the salient points, train the brain, move on.

A model? It ingests a thousand books, atomizes the contents into vectorized memory, and regurgitates that knowledge on demand. That is not learning. It is extraction at scale.

The ruling draws a legal distinction between licensed and pirated works, which is important but it’s also a distraction. The bigger issue is not where the content came from, it’s how it’s used and who gets a say in that process.

If you are a creator…author, photographer, designer, musician, videographer…you should be paying very close attention. Because today’s "fair use" argument might become tomorrow’s excuse to swallow your life’s work into a generative system with no attribution, no compensation, and no control.

The Illusion of Consent

Let’s be blunt. Most people never consented to their data being used this way. They did not license their work to be copied, transformed, and served back as synthetic content. They certainly didn’t agree, regardless of the endless legalese stamped on every website, to train a model that might eventually compete with them so let’s call it what it is: theft.

This is not just a copyright issue. It is a power imbalance between the creators of knowledge and the corporations profiting from it.

Enterprise Leaders: Read the Fine Print

If you’re building AI systems or using models trained on third-party data, this isn’t a theoretical debate. It’s a compliance risk. A PR risk. A governance risk.

Do you know what went into your model? Can you trace the data lineage? If it turns out the training data includes works scraped without permission…even legally purchased ones…how confident are you that you’re on safe ground? Today that slope is slipperier than the Tail of the Dragon (on US 129) after an ice storm: all curves, no grip, and nowhere to bail out.

When I wrote the white paper about my daughter’s brain tumor, I had two full pages of QA questions and ran that content through three different platforms to make sure I wasn’t infringing or plagiarizing any part of that work. Then I cited every single instance where I referenced someone else’s research.

We all say we respect the work of others. The question is: do our AI systems?

This Is Not a Side Issue. This Is the Issue.

Ownership and use of data isn’t just a pivotal topic in AI. It is the pivotal topic. Every other breakthrough from smarter chat to faster reasoning to adaptive agents depends on where the knowledge came from and who had a say in how it was used.

We cannot get this one wrong. If we do, it undermines trust in everything else we build.

I don’t have all the answers but I do know this: The future of AI must be built on respectful, transparent data practices. Anything less compromises not just the law, but the trust that powers innovation.