An inside look at X’s Community Notes | Keith Coleman & Jay Baxter

Executive Summary
Lenny's Podcast episode, sponsored by WorkOS and Productboard, in which Lenny Rachitsky talks with Keith Coleman, product lead for Community Notes, and Jay Baxter, its founding ML engineer, about how X's crowdsourced fact-checking actually works. They explain the core mechanic: anyone can propose a note on a misleading post, others rate it, and the algorithm looks for agreement from people who have disagreed in the past - that surprising cross-aisle consensus is what makes notes neutral, accurate and well written. They cover the principle that all of humanity participates, with no exemptions for Elon, government figures or even advertisers, the internal Twitter contest from which the algorithm emerged, how the product survived four leadership eras including Jack and Elon, and independent studies finding that notes change whether people agree with a post's core claims - which is why Meta adopted the model. PARTIAL COVERAGE: this summary highlights the mechanism and principles; algorithmic details and later segments are not covered. These are the guests' own claims and cited studies, not independently verified.
Key Takeaways
The core mechanic
Propose, rate, then show only notes found helpful by people who normally disagree.
Surprising agreement
Cross-aisle consensus is what makes notes neutral and accurate, they argue.
No exemptions
Elon, government figures and advertisers are all eligible for notes.
All of humanity
The philosophical stance: everyone participates, and the data shows where people stand.
Born of a contest
The algorithm emerged from an internal Twitter contest and outlived four leadership eras.
Notes change minds
Independent studies found notes shift agreement with a post's core claims.
Meta adopted it
The model is now Meta's main fact-checking approach too.
The Conversation Breakdown
The core mechanic
⏱ Untimed topicPropose, rate, then show only notes found helpful by people who normally disagree.
Surprising agreement
⏱ Untimed topicCross-aisle consensus is what makes notes neutral and accurate, they argue.
No exemptions
⏱ Untimed topicElon, government figures and advertisers are all eligible for notes.
All of humanity
⏱ Untimed topicThe philosophical stance: everyone participates, and the data shows where people stand.
Born of a contest
⏱ Untimed topicThe algorithm emerged from an internal Twitter contest and outlived four leadership eras.
Notes change minds
⏱ Untimed topicIndependent studies found notes shift agreement with a post's core claims.
Meta adopted it
⏱ Untimed topicThe model is now Meta's main fact-checking approach too.
Core Frameworks & Ideas
Bridging algorithm - the Notes mechanic
Surface only what earns agreement across historical disagreement.
Universal eligibility - their neutrality rule
Credibility comes from exempting nobody, including leadership and advertisers.
Small-team leverage - Coleman's lesson
A tiny team with a clear principle outproduces large unfocused ones.
Actionable Next Steps
- ✓Ask where your product could use bridging instead of majority vote.
- ✓Define one principle your team would keep under any new leadership.
- ✓Find the independent evidence that your core mechanism works.
- ✓Test whether your rules apply to your own most powerful users.
Notable Quotes
“We actually look for agreement from people who have disagreed in the past”
“we want all of humanity to participate”
“We shouldn't exempt Elon”
The One Thing to Remember
Community Notes' trick: don't seek consensus - seek agreement between people who usually disagree, and exempt no one, not even Elon.