Thomson Reuters v. Ross Intelligence: AI Ate Its Competitor’s Homework
Some AI copyright cases involve artists staring in horror at machines generating suspiciously familiar pictures.
Some involve novelists discovering their books inside gigantic training datasets.
This one involved lawyers.
Naturally, things became expensive.
Thomson Reuters, owner of Westlaw, sued Ross Intelligence, an AI legal-research startup that wanted to challenge Westlaw in the legal-search market.
Ross needed data to train its system.
Westlaw had an enormous amount of extremely useful legal data.
There was only one small problem.
Westlaw belonged to Ross’s competitor.
Ross asked Thomson Reuters for a license.
Thomson Reuters said no.
What happened next eventually produced one of the most important early American court decisions about artificial intelligence and copyright.
And it did not go well for Ross.
Westlaw Is More Than a Pile of Court Decisions
The underlying law is public.
Judicial opinions themselves are not copyrighted by Thomson Reuters.
Anybody can read them, collect them, analyze them and build tools around them.
But Westlaw does considerably more than dump court opinions onto a website.
Its lawyer-editors create headnotes summarizing important points of law and organize legal material through the West Key Number System.
That editorial layer is valuable precisely because the raw material is enormous.
A lawyer does not merely want three million court decisions.
A lawyer wants the right decision.
Now.
Preferably before the client calls again.
Westlaw's editorial machinery helps make that possible.
And Thomson Reuters claims copyright in its copyrightable editorial material.
Ross Wanted an AI Lawyer’s Search Engine
Ross Intelligence had an attractive idea.
Instead of navigating traditional legal databases, lawyers could ask legal questions in natural language and receive relevant cases.
An AI-powered legal research system.
Today that sounds almost routine.
At the time, it was ambitious enough to threaten established legal-research companies.
But artificial intelligence needs training data.
Ross needed large numbers of legal questions paired with useful answers.
So Ross approached Thomson Reuters about licensing Westlaw content.
Thomson Reuters refused.
Not surprisingly.
Ross was trying to build a competing product.
Selling your competitor the ingredients it needs to replace you is not a universally admired business strategy.
So Ross found another route.
Enter the Bulk Memos
Ross contracted with LegalEase to produce training material.
LegalEase created roughly 25,000 “Bulk Memos” consisting of legal questions and good and bad answers.
The lawyers creating those materials were given guidance involving Westlaw headnotes.
They were told not simply to copy and paste the headnotes.
But the Westlaw material was nevertheless used in constructing the training data that Ross ultimately purchased and fed into its AI system.
That distinction became the battlefield.
Ross argued, among other things, that its use was fair use.
After all, the Westlaw headnotes were not simply being displayed to Ross customers as Westlaw headnotes.
They were being used at an intermediate stage to train an artificial-intelligence search system.
That sounds considerably more sophisticated.
Unfortunately for Ross, sophisticated is not synonymous with fair.
The Judge Changed His Mind
There is an unusually human detail buried inside this case.
Judge Stephanos Bibas had previously declined to resolve much of the fair-use dispute at summary judgment.
Then he looked at the case again.
More closely.
And changed his mind.
His February 2025 opinion begins with an observation rarely associated with judicial ego:
“A smart man knows when he is right; a wise man knows when he is wrong.”
The judge concluded that his earlier ruling had not gone far enough.
He granted Thomson Reuters partial summary judgment on direct copyright infringement and granted Thomson Reuters summary judgment against Ross on fair use.
That made the decision one of the first major American judgments directly confronting fair use in an AI-training context.
And the reasoning matters far beyond legal databases.
Training an AI Is Not a Magic Spell
Ross had an argument familiar across the AI industry.
The copyrighted material was used as part of an intermediate computational process.
The finished product was not simply a pile of copied Westlaw headnotes.
Ross transformed the material into numerical information about relationships among legal words and used that information in its AI system.
In other contexts, intermediate copying has sometimes received fair-use protection.
But the court looked beyond the technological machinery to something brutally ordinary:
What was the commercial purpose?
Westlaw helps lawyers find relevant law.
Ross was building a tool to help lawyers find relevant law.
Westlaw charged money.
Ross intended to charge money.
Westlaw and Ross were competitors.
The court concluded that Ross's use was not transformative.
Different technology.
Very similar purpose.
That distinction is devastatingly important.
Putting copyrighted material through an AI training pipeline does not automatically transform the commercial purpose of using it.
You cannot necessarily turn infringement into fair use simply by inserting a neural network in the middle.
The Competitor Problem
The fourth fair-use factor examines the effect of the use on the market for the copyrighted work.
Here Ross had an exceptionally awkward fact.
It was trying to compete with Westlaw.
The court described Ross's product as a potential “market substitute.”
That is copyright-law dynamite.
Imagine the argument stripped of all AI terminology:
Company A spends years and enormous amounts of money creating valuable editorial material.
Company B wants to compete with Company A.
Company B asks to license Company A's material.
Company A refuses.
Company B nevertheless obtains training material derived from Company A's work and uses it to build the competing product.
Then Company B argues fair use.
Suddenly the neural networks seem almost incidental.
This starts looking less like a philosophical argument about machines learning from human culture and more like an old-fashioned business fight.
This Was Not ChatGPT
There is an important limitation.
Ross was not a generative-AI system like ChatGPT, Claude, Midjourney or Suno.
The court explicitly noted that distinction.
Ross's AI did not write new legal opinions or generate new prose for users.
A user entered a legal question, and Ross returned relevant judicial opinions that already existed.
That means nobody should read Thomson Reuters v. Ross as a universal ruling that all AI training on copyrighted material is illegal.
It isn't.
Later American cases involving generative AI have already produced different fair-use outcomes.
The facts matter.
The market matters.
The source of the training material matters.
And, increasingly, the relationship between the copyright owner and the AI company matters.
Fair Use Has Teeth
The AI industry's public discussion of copyright sometimes makes fair use sound like an automatic exemption.
Train first.
Say “transformative.”
Continue operating.
Ross demonstrates why that is dangerous.
Fair use is not a technology.
It is a legal defense evaluated through multiple factors.
A court asks why the material was used.
What kind of material it was.
How much was taken.
And what the new use does to the existing or potential market.
Ross actually prevailed on two of those four factors.
It still lost the overall fair-use analysis.
Why?
Because the court considered the first and fourth factors particularly important.
Ross's use was commercial and non-transformative.
And Ross was trying to create a competing substitute.
Sometimes two bad factors are worse than two good ones.
AI Does Not Abolish Competition Law’s Reality
There is a broader lesson here for AI businesses.
A company may describe what it is doing in technological language:
training,
embedding,
vectorizing,
modeling,
machine learning,
semantic retrieval.
A court may eventually translate all of that back into English.
You took something valuable from a competitor and used it to build a product competing with them.
That translation can hurt.
The technology may be revolutionary.
The business relationship is ancient.
Competitors have been copying each other's homework since long before anybody invented silicon.
The Case Is Not Finished
There is another important qualification.
The February 2025 ruling did not simply close the courthouse doors and end every issue in the litigation.
Questions remained, and Judge Bibas later certified important copyright and fair-use issues for interlocutory appeal and stayed the case while seeking appellate guidance.
He nevertheless explicitly stood behind his February reasoning.
So Thomson Reuters v. Ross should be understood for what it is:
a major district-court fair-use ruling in an AI-related copyright dispute — not the final word from the U.S. Supreme Court on artificial intelligence.
That final word may be years away.
The Most Dangerous Training Data May Belong to Your Competitor
The Ross case is fascinating precisely because it strips away much of the emotional scenery surrounding AI copyright.
No starving illustrator.
No novelist screaming about pirate libraries.
No Mickey Mouse.
No beloved song emerging from a machine.
Just two businesses.
One owned expensive information.
The other wanted to build a better machine for selling access to similar information.
And somewhere between them sat 25,000 training memos and a pile of Westlaw headnotes.
That makes the lesson unusually clean.
AI training is not automatically fair use.
Intermediate copying is not automatically fair use.
Calling something “machine learning” does not automatically change its commercial purpose.
And building an AI competitor from material derived from the company you intend to compete with is the kind of fact judges tend to notice.
Ross wanted to teach a machine how to do legal research.
Instead, it helped teach the AI industry an early lesson in copyright law:
Don't eat your competitor's homework and expect “fair use” to make the bill disappear.
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