AI vs. Copyright: Is Training Data Fair Use or Theft?
AI is gobbling up the internet like an all-you-can-eat buffet—but is it dining legally or just stuffing its pockets with stolen snacks? As OpenAI and DeepSeek lock horns over training data, we’re diving into one of the most pressing legal and ethical battles of the AI age. Is AI learning like a human (absorbing knowledge and creating something new), or is it just a hyper-efficient copy-paste machine wrapped in a layer of machine learning jargon?
Welcome to another edition of the best damn newsletter in human-centric innovation.
Here’s what we’re covering today:
→ The OpenAI vs. DeepSeek controversy
→ What fair use really means in the AI era
→ How copyright laws need to evolve (before the lawsuits get completely out of hand)
Let’s get to it! 👇
The OpenAI vs. DeepSeek Controversy
Imagine two AI companies in a standoff, pointing lawsuits at each other like an old-school Western—except instead of revolvers, they’re firing legal jargon. That’s the situation between OpenAI and DeepSeek.
DeepSeek, a Chinese AI startup, has been accused of training its DeepSeek R1 model using OpenAI’s proprietary data—without permission. Microsoft (a major OpenAI investor) suspects DeepSeek used "model distillation," a sneaky process where a smaller AI learns from a bigger one by studying its responses. Think of it as the AI equivalent of peeking at your smarter classmate’s test answers.
Here’s where things get messy: OpenAI itself is under fire for allegedly doing the exact same thing. Authors, musicians, and media companies have lined up with lawsuits, claiming OpenAI used their copyrighted works without consent. So, when OpenAI cries foul over DeepSeek’s methods, it raises a bigger question: where’s the line between learning and stealing?
Understanding Fair Use (Or: Can AI Really Call It "Studying"?)
"Fair use" is a legal concept that sometimes allows people (and, apparently, AI) to use copyrighted material without permission. It’s typically reserved for things like criticism, parody, and education—not for building multi-billion-dollar AI empires.
The law weighs four main factors:
Purpose & Character – Is the use transformative? (Did you remix it into something new, or did you just copy-paste?)
Nature of the Original Work – Is it factual or creative? (Hint: AI training on news articles is easier to justify than using someone’s fantasy novel.)
Amount & Substantiality – How much did you use? (Did you take a few lines or the whole thing?)
Effect on the Market – Are you making money in a way that undercuts the original creator? (Big issue when AI-generated content starts competing with human work.)
On paper, fair use should help balance innovation with creator rights. In practice? It’s a legal grey zone the size of a football pitch, and AI companies are sprinting through it at full speed.
The Debate Around AI Training
The “It’s Fair Use” Argument
AI companies argue that models don’t copy text—they learn from it, much like a human does. If a person reads 500 books on history and writes their own version, that’s called being well-read. AI companies say their models are doing the same thing, just much faster (and without needing coffee breaks).
Restricting AI training, they argue, would stifle innovation. After all, AI’s ability to generate useful content—from code to creative writing—depends on the vast amount of data it has access to. No data, no innovation.
The “It’s Straight-Up Theft” Argument
On the other side, critics point out that AI-generated content doesn’t just "learn" from copyrighted works—it often mimics them in a way that’s... uncomfortably close. If an AI model spits out text that sounds exactly like The New York Times or replicates an artist’s distinct style, where’s the originality?
This is why lawsuits are piling up. Sarah Silverman, The New York Times, and a growing list of authors and musicians are suing AI firms, arguing that their work is being used without permission or compensation. If an AI can generate work that competes with the original, is it fair use—or just another way for tech companies to profit off others' creativity?
Final Thoughts: A Legal Battle That’s Only Just Beginning
The OpenAI vs. DeepSeek showdown highlights a bigger issue: AI is built on mountains of data, and nobody really knows where the legal line is. Should AI be treated like a student learning from vast amounts of information, or is it just mass-producing derivatives of copyrighted work without giving credit where it's due?
One thing’s certain: this debate isn’t going away anytime soon. The question now is whether the law can keep up before AI companies rewrite the rules themselves.
Join the Discussion: Where do you stand? Should AI be allowed to train on copyrighted data, or should creators have more control over their work? Reply with your thoughts in the comments—we’d love to hear from you.
Go Beyond the Headlines
The OpenAI vs. DeepSeek battle is just one piece of the AI puzzle. From copyright to ethics, AI is reshaping industries fast—but do you really understand how?
I've just launched a new course at the Netropolitan Academy titled AI Uncovered breaks it all down, cutting through the jargon so you can grasp AI’s real impact—without needing a tech background. Learn how AI works, why Fairness, Accountability, and Transparency matter, and what it means for the future of work and creativity.