We coded 322 YouTube comments on Amanpour and Company's interview with Heidy Khlaaf, who argues the "rogue AI" story is the wrong one. Most of the audience agreed with her, got angrier as the thread aged, and asked for regulation without being prompted.
Read moreCommon Signals helps people explaining AI know which messages
actually work.
We provide research, tools, training and workshops to help people talking about AI know what actually gets through to their audiences.
We focus on these questions
What do different publics actually understand and misunderstand about AI? How is that shifting?
What makes AI communication backfire or fail to land? How do communicators learn from this?
Which messages and messengers are proven to work? How do we get them into wider use?
How do we build lasting public understanding of AI?
We turn public attitudes into tested, practical guidance.
Research
In‑depth qualitative and quantitative research
- Quantitative and qualitative methods that turn public attitudes into clear, usable insight
- Audience segments and a messenger map, so any organisation can see how to reach the people it needs to
Guidance & training
Practical strategy
- Communication strategies built from tested evidence
- Clear, jargon‑free guides any comms team can pick up and use
- Independent review of messaging, research methods and materials
Tools
A free, open toolkit
- Tested message guides, broken down by audience segment
- A messenger map and plain‑English summaries of the research
Workshops
Working sessions
- Reviewing your own materials against the tested findings
- Working through message choices for the audience you're trying to reach
Featured research
We coded 1,245 YouTube comments on the Dwarkesh Podcast's interview with METR's Ajeya Cotra about the OpenAI agent-swarm incident. The audience accepted the danger, joked about it, praised the messenger, and had no idea what to do next.
Read more
We compared Reddit comments on the OpenAI and Hugging Face incident before and after METR and Redwood Research published an independent investigation. Afterwards, fewer people called it a stunt and more accepted it happened. The ones still calling it a stunt got harder to shift.
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We coded 1,534 Instagram comments on a one-minute reel by Nate Soares of the Machine Intelligence Research Institute (MIRI). Most commenters accepted the argument. Many said it was already too late, and almost nobody asked what to do.
Read moreWhy Common Signals
Global AI investment reached around $581.7bn in 2025 [1]. A sliver of that goes to safety. A sliver of the sliver goes to helping the public understand any of it.
$581.7bnglobal AI investment, 2025
<0.1%share going to safety
Grounded in evidence
The people trying to explain AI mostly work from instinct. Early evidence, including the Seismic Foundation's 2025 study [2], suggests the field's dominant framings underperform.
Neutral by design
No policy position, no campaigns, no proprietary advice. Findings go to every side of the AI conversation, which is what lets one body credibly serve both.
A proven model, applied early
Built on proven methods and applied to AI while the gap is still wide open.