Charity Majors. Co-founder and CTO of Honeycomb.io, the observability platform built on wide-event telemetry and the company that codified the modern definition of observability as the ability to ask arbitrary questions of your production systems without shipping new code. Built and ran distributed infrastructure at Parse (acquired by Facebook) and Linden Lab. Co-authored Observability Engineering and Database Reliability Engineering for O'Reilly. Writes candidly about engineering management, reliability, and AI adoption at charity.wtf.
Operating themes
- AI adoption as an organizational design problem AI tool rollout creates asymmetric information and missing feedback loops that require deliberate engineering to resolve, not just technical deployment.
- Observability as the prerequisite for agency You cannot improve what you cannot measure. This applies to AI productivity and organizational health as much as to system reliability.
- Engineering discipline precedes AI-driven productivity gain Teams that already have strong feedback loops, measurement culture, and engineering rigor benefit from AI. Teams without those properties do not see the same gains. AI amplifies what is already there.
Cards
- AI adoption creates an asymmetric information problem where wins are public and costs are private, requiring deliberate feedback loop design, AI adoption creates an asymmetric information problem: wins are public, costs are private [Tier B]