Charity Majors

Things Charity Says on Podcasts

cofounder and CTO of honeycomb.io; pioneered modern observability. co-author of O'Reilly books "Database Reliability Engineering" and "Observability Engineering", now wrapping up the 2nd ed. loves free software, free speech, and peaty single malts.

Where to Find Them

Charity Majors writes charity.wtf , hosts O11ycast , writes Console DevTools , writes Refactoring and writes Code Like A Girl . They have also been a guest on Arrested DevOps (4 times) , The Pragmatic Engineer (3 times) , Oxide and Friends (2 times) , Tech Lead Journal , Semaphore Uncut , Test & Code , .NET Rocks! , The New Stack Podcast , CoRecursive: Coding Stories , Hanselminutes with Scott Hanselman , The Stack Overflow Podcast , Scaling DevTools , The Bike Shed , The Aboard Podcast , The InfoQ Podcast , Maintainable , Kubernetes Podcast from Google , Soft Skills Engineering and Software Misadventures .

Recently: “It’s ok that computers are (sometimes) better than us” on The Stack Overflow Podcast (October 2026); “If your team is happy, are you doing a good job?” on charity.wtf (October 2026); “Charity Majors: Embrace Your Computer’s Chaos” on The Aboard Podcast (September 2026); “Where do AI norms come from?” on charity.wtf (September 2026); “Confessions of an Unrepentant Slop Snob” on charity.wtf (September 2026); “Headed for the Exit: the Great Engineering Leader Career Break” on The Pragmatic Engineer (August 2026).

What They Said

Charity Majors has just argued that AI can now produce code at roughly median-engineer quality, so code can be generated in huge numbers and compared. Here she describes how software would need to be re-architected so people can still understand it. She says the evals, which define what good looks like, would last longer than the code itself.

The Stack Overflow Podcast · 2026-10-30 Permalink → Listen →
The Stack Overflow Podcast Around 03:34 into the episode
Charity Majors

…at the same level of quality as the median software engineer, which means that while one software engineer can output a piece of work that works, AI Can generate a thousand of them, 10,000. We can see which one is the best. You know, it just, it makes you think very differently about code itself.

Ryan Donovan

It's interesting where it's where you spin up all these nodes, now you can spin up all these versions of that feature and compete them against each other.

Charity Majors

That's one of the building blocks, I think, because it's pretty clear to me that in the same way, we don't expect every software engineer to understand how to debug types of RAM or how many CPUs or if there's a device. We just don't expect them to understand that. We used to. We don't anymore. And I don't think it's an exact analogy, but I do think that when we talk about cost of software as defined by its maintenance, we're talking about the cost of having to understand everything. And I think that if we re-architect our software so that we can understand it at the level of functions instead of lines, if you re-architect it so that there are bounded parts that can be cut. And of course, the most durable part of this system would be the evals, not the code itself. The evals describe what must be true for this to be defined as good. And then, yeah, we can generate 10,000 disks and see which one is the best, you know?

Ryan Donovan

Yeah, yeah. And for those evals, are you thinking like the LLM as a judge sort of evals, or do you have a deterministic eval?

Charity Majors

Well, both. And I mean, a lot of these tools actually already exist. Conformance testing. They're all in the realm of ops and QA, which are historically like the cost centers. You're not quite as cool as software engineers. But like they're aimed at describing the world as it is instead of the…

Ryan Donovan

Jez Humble had a talk yesterday, sort of specifically targeting the Strangler Fig architecture.

Speaker names come from our own diarization. The position is estimated from where the line sits in the episode.

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