NYT AI tool for tracking the manosphere — Nieman Lab case study

The New York Times built an internal AI tool called the Manosphere Report that transcribes, summarizes, and synthesizes content from approximately 80 right-wing podcasts daily. The tool delivers a morning email at 8 a.m. ET to nearly 40 reporters, flagging emerging themes, shared talking points, and shifts in rhetoric across the monitored media ecosystem.

Context

The tool was built by the Times’ AI Initiatives Team, a small newsroom unit launched in 2024 that has focused on using AI for data analysis and investigative reporting rather than for generating articles. The Manosphere Report grew out of an earlier tool called Cheatsheet, which handles basic transcription and summarization.

The system works in layers: each podcast episode is automatically transcribed and summarized. Every 24 hours, the individual summaries are collated into a meta-summary that identifies shared talking points and notable daily trends. Reporters use these summaries as signals for when sentiment or rhetoric is shifting across the monitored media, but they always go back and listen to the original audio before reporting.

Key takeaway

The Times treats AI as a research and monitoring tool rather than a writing tool. Zach Seward, the editorial director of AI initiatives, frames the philosophy clearly: creating new text for publication is not the most effective use case for generative AI in a newsroom. The real value is helping reporters process information at a scale that would be impossible manually.

Who should read this

Editors, journalists, and content strategists interested in how a major newsroom uses AI to expand reporting capacity rather than replace reporters. Also relevant for anyone managing a content monitoring workflow across multiple sources.