Beyond the Hype: Honeycomb's CTO on How AI Really Impacts Engineering Teams

Charity Majors is the CTO of Honeycomb, an observability platform used by product teams at the world's top tech companies, like Dropbox and Intercom.

As a part of our AI Speedrun series, we asked her how her team at Honeycomb builds with AI. Below is a recap of the highlights from our conversation.

AI means experimenting more 

We asked Charity where AI is actually accelerating engineering. For her team, especially on front-end and design, it’s in the ideation phase.

The ideation phase, when you're throwing away a lot of code anyway, has gotten a lot faster.

Before, someone might spend a whole week going down one path just to realize it doesn’t work. Now, they can do that in an afternoon. Honeycomb’s engineering velocity has improved because they’re spending more time on the right things.

But how do you know it's the right thing? You ship it to prod.

At Honeycomb, production is the ultimate source of truth. AI helps get features into users’ hands faster. And if it doesn’t land in prod, it doesn’t count.

Great engineers are great communicators

AI has changed a lot at Honeycomb, but one thing it hasn’t changed is how they hire. Many orgs are focusing on specific AI oriented skills, but the team at Honeycomb is sticking to what they’ve always prioritized: communication.

We've always indexed heavily on communication skills. That’s never been more true than with AI.

During technical interviews, engineers get the usual take-home assignment, but then they also have to explain their decisions, trade-offs they made, and their overall thought process behind the code. According to Charity, engineers who can articulate clearly are engineers who can actually leverage AI meaningfully.

The future of software: disposable vs. indispensable

Charity thinks software will fall into two distinct types:

1. Disposable software: quick experiments vibe-coded rapidly and discarded just as easily.
2. Indispensable software: critical infrastructure that has to be reliable, with robust observability and maintenance. 

There is a future where both these categories exist in parallel. AI has enabled disposable software - fast, experimental, low-risk tools - but indispensable software will still reign, especially in domains where there's small room for error: logistics, healthcare, etc. This category of software will always require skilled engineers and SREs to ensure reliability and safety.

There's always going to be a place for cynics in the engineering world—those who ask, ‘What's going to happen when this crashes into reality?’

The future of engineering tools

Builders will increasingly rely on one integrated environment, likely agent-driven IDEs via MCPs or some other protocol that keeps them in flow state and reduces context-switching.

There’s a lot of noise online around when this might actually happen and what engineering orgs should do about it, but Charity doesn’t care about the hype. She’s just focused on setting up the right workflows, instead of obsessing over standalone interfaces. If her team has the right workflows in place, they can plug in to any future agentic protocol or platform fairly quickly. 

The most important surface for engineers is their workflow. If you focus there, you'll be fine.

We had a blast jamming with Charity! Check out similar conversations with product and engineering leaders at top engineering orgs (like Vercel, Wix, Intercom, etc) to unpack how they actually build with AI. Full episodes on YouTube.