News Analysts, Reporters, and Journalists

AP has auto-generated thousands of quarterly earnings stories per year since 2014 using Wordsmith; Bloomberg, Reuters, and the Washington Post (Heliograf) run similar pipelines for structured-data stories. If your beat is primarily structured-event coverage — league standings, quarterly results, meeting minutes — your output is already heavily automated. Shift toward the commentary, context, and accountability layer: what does the earnings report mean for workers, regulators, or competitors? That interpretive judgment is what editors now want from staff reporters on automated beats.

Interview transcription — once a one-to-one time cost — is now 90-96% accurate at near-zero marginal cost with Otter.ai or Trint. Your value on transcription is not typing speed; it is the judgment about which quotes are quotable, what the source meant versus what they said, and whether the on-record content clears your outlet's verification bar. Build a quote-review workflow: AI transcribes, you evaluate.

AI background-research tools (Perplexity, NotebookLM, ChatGPT) have dramatically compressed the time cost of background research — tasks that took a full day in a library or database can now be done in under an hour. The critical constraint is that these tools hallucinate citations and misattribute claims: every AI-surfaced fact must be traced to a verifiable primary source before appearing in a published story. Develop a citation-chain discipline: AI finds the lead, you confirm the fact at the source.