Stop being skeptical about AI for development with Charity Majors

2026-08-12

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Brought to You By:• Antithesis – verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages.• WorkOS – everything you need to make your app enterprise ready.• Buildkite – CI software built to absorb whatever your coding agents throw at the build queue—In 2025, it was rational to be skeptical about AI, but in 2026 it’s clear that AI is changing all of the industry, and there’s less an...

Brought to You By:

• Antithesis – verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages.

• WorkOS – everything you need to make your app enterprise ready.

• Buildkite – CI software built to absorb whatever your coding agents throw at the build queue

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In 2025, it was rational to be skeptical about AI, but in 2026 it’s clear that AI is changing all of the industry, and there’s less and less place for skepticism. This take is from one of my favorite voices in software reliability and observability: Charity Majors, CTO and cofounder of Honeycomb, co-author of Observability Engineering. (Note: the second edition of Observability Engineering is out, and it’s pretty much a full rewrite of the book, I recommend grabbing it if you’re building reliable systems)

In this episode, I sat down with Charity to discuss how her thinking on AI has evolved, why she believes it is becoming a foundational part of software engineering, and what that means for how teams build, review, and ship software.

We explore how AI is changing the economics of code generation, why reliability and verification are increasingly the bottlenecks, and why the rise of non-deterministic systems requires more engineering discipline. Charity shares her views on code reviews, observability, DevOps, leadership, and why both AI skeptics and enthusiasts are getting important things right.

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Timestamps

00:00 Intro

02:56 How Parse led to Honeycomb

06:00 The limits of individual productivity metrics

09:08 How Charity’s perspective on AI has evolved

13:50 Rewriting code vs. editing code

19:20 Production as a stage of development

22:14 Code reviews

26:56 Non-deterministic systems

31:11 Sensible uses of AI

37:41 The two AI camps