Stop being skeptical about AI for development with Charity Majors

August 12, 2026

AI Summary

5 min read

Charity Majors, co-founder and CTO of Honeycomb, watched the same AI trajectory many engineers did: initial skepticism, a moment of recalibration in late 2025, and then a settled conviction that the industry is past the point of debating whether AI-generated code will be shipped unread by humans. The real question, she argues, is when — and what engineers must build to make that safe. Her central claim is that software engineering is experiencing a shift as consequential as the move from pets to cattle in infrastructure: when rewriting code becomes cheaper than editing it, the entire discipline needs to adopt the validation mindset of operations and QA, not the authorship mindset that has defined the profession for decades.

The trust account and why code review is overloaded

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What you'll learn

1 (00:00) **Introduction: The Two Camps on AI** - Charity Majors, co-founder and CTO of Honeycomb, sets up the central tension: engineers who hate AI vs. those who embrace it, and argues they are talking past each other.
2 (05:03) **Productivity Measurement Then and Now** - A throwback to a 2020 blog debate on measuring individual developer productivity, revisited in the age of AI.
3 (08:19) **2025: AI’s “2010 for the Cloud” Moment** - Charity reflects on how her own perspective shifted from seeing AI as a big feature to a generational change.
4 (11:27) **The Central Thesis: Shipping Code You Haven’t Read** - The main mechanism of the argument: we will eventually be comfortable shipping code we didn’t read, and the path is through rigorous testing and validation, not manual review.
5 (15:03) **Lessons from Ops and QA: Humility and the Production Mindset** - Software engineering has historically been snobbish about operations and QA, but AI forces a reckoning with their methods.
6 (22:24) **The Overloaded Concept of Code Review** - Code review bundles many functions, some of which are better done elsewhere, especially when AI writes the code.
7 (26:10) **Non-Deterministic Systems Require More Discipline** - AI is a non-deterministic tool, and that forces a new level of rigor in testing, evals, and guardrails.

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Show Notes

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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

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

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