Brought to You By: • Antithesis [https://antithesis.com/pragmatic] – verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages. • WorkOS [https://workos.com/] – everything you need to make your app enterprise ready. • Buildkite [http://buildkite.com/pragmatic] – 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 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 [https://www.oreilly.com/library/view/observability-engineering-2nd/9781098179915/]. (Note: the second edition of Observability Engineering [https://www.oreilly.com/library/view/observability-engineering-2nd/9781098179915/] 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. — 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 44:40 Why AI works so well for building software 49:42 DevOps 55:13 Modern observability 1:00:40 Handling context overload 1:01:56 What’s new in Observability Engineering’s 2nd edition 1:07:45 What effective leadership looks like 1:10:25 Engineering management: what is changing? 1:16:31 Junior engineers 1:18:01 AI fatigue 1:21:39 Book recommendations — The Pragmatic Engineer deepdives relevant for this episode: • Shipping to production [https://newsletter.pragmaticengineer.com/p/shipping-to-production] • Deepdive: How 10 tech companies choose the next generation of dev tools [https://newsletter.pragmaticengineer.com/p/measuring-ai-dev-tools] • Why is Meta destroying its engineering organization? [https://newsletter.pragmaticengineer.com/p/why-is-meta-destroying-its-engineering] • When AI writes almost all code, what happens to software engineering? [https://newsletter.pragmaticengineer.com/p/when-ai-writes-almost-all-code-what] • Are AI agents actually slowing us down? [https://newsletter.pragmaticengineer.com/p/are-ai-agents-actually-slowing-us] • Observability: the present and future [https://newsletter.pragmaticengineer.com/p/observability-the-present-and-future], with Charity Majors • The third golden age of software engineering – thanks to AI [https://newsletter.pragmaticengineer.com/p/the-third-golden-age-of-software], with Grady Booch — Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠ [https://penname.co/]https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠ [https://newsletter.pragmaticengineer.com/p/engineers-leading-projects]. For inquiries about sponsoring the podcast, email podcast@pragmaticengineer.com. Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe [https://newsletter.pragmaticengineer.com/subscribe?utm_medium=podcast&utm_campaign=CTA_4]