Hiring in the Age of AI: What to Interview For

The following is an excerpt from the book Vibe Coding: Building Production-Grade Software With GenAI, Chat, Agents, and Beyond by Gene Kim and Steve Yegge.

In traditional software hiring, interviews typically centered on things like languages mastered, frameworks used, algorithms memorized. But as the rise of AI is changing coding, it’s also changing what makes an engineering candidate stand out. Today, as leaders in professional vibe coding, we find ourselves asking: What should we be interviewing for?

Here’s what we think: In almost all cases, you should first look for candidates who have already jumped into the AI pool. If someone hasn’t at least experimented with vibe coding yet, that’s a potential red flag. If you interview a chef who’s never tasted garlic or salt, you’ll want to understand why. They might have a compelling story, perhaps coming from a job where seasoning was forbidden, but you’d still wonder if they have enough curiosity to learn about vibe coding. 

We’d ask: Have they played with chat assistants and coding agents? Can they tell you what worked, what didn’t, and why they’re excited (or skeptical)? Their responses will reveal more about their mindset than dozens of checklist questions ever could.

We’re not suggesting you only hire maniacs who write ten thousand lines of AI-assisted code before breakfast. Rather, it’s about identifying engagement, interest, and curiosity. As you now know, vibe coding pushes developers higher up the abstraction ladder.

Communication skills, once a nice-to-have, are now non-negotiable. In vibe coding, precise communication determines many things, including your productivity, your outcomes, and—frankly—how frustrating your day might be. Candidates need to describe problems effectively, give clear context, provide actionable feedback, and direct AI assistants toward solutions without costly misunderstandings.

Another skill is the ability to read and review code at scale. With vibe coding, you might write thousands of lines of code a day, like Steve has done—but did you realize this requires reading and understanding nearly ten times that many lines of code? That’s like reading all the source code of a different medium-sized OSS project every single day. Doing this, he has spotted many subtle problems, including a persistent rogue code deletion that AI wouldn’t stop attempting. And many of the woes he has experienced would have been caught if he had been paying more attention.

We highly recommend that you conduct practical assessments involving AI interaction. Invite candidates to solve problems using AI coding assistants. This isn’t cheating—this is literally how they’ll do their job. We would go so far as to interview them on coding assistants and determine how proficient they are with at least one. They’re rapidly becoming the new IDEs, and for now, are an important adjunct to IDEs. 

Observe your candidates carefully: Are they thoughtful in framing their prompts, adept at managing context, savvy at debugging model misunderstandings? Or are they flailing or fumbling around? Are they imprecise, recklessly accepting AI suggestions, or overly dependent on AI to do their thinking for them?

Seniority may matter less now, because vibe coding is new territory for everyone. Junior developers and veteran engineers alike are scrambling up this learning curve together. What counts is how enthusiastically they’re climbing and how fast they’re learning.

Who knows where the next set of obvious practices will come from? Kent Beck recently speculated, “There’s going to be a generation of native [vibe] coders, and they’re going to be much better than we are at using these tools.”¹ It would sure be a shame if you interviewed them and passed because they didn’t fit your image of what a good programmer was.

One last note: From Steve’s experience, he recommends at least one in-person interview, and when coding skill matters, one air-gapped interview with no AI assistance allowed. This practice will avoid accidentally hiring candidates who cannot code at all without AI (by late 2025, it will be a huge red flag), and/or actual AIs who are interviewing for the job (an increasingly common problem).

We’ve seen firsthand how vibe coding transforms what matters in hiring, so adjust your interview filters accordingly. Whether you’re hiring for one role or reshaping your organization around engineering with vibe coding, this is your recipe for assembling an organization full of great new leaders.

For more insights on effective AI-assisted development, check out Kim and Yegge’s new book Vibe Coding and their podcast Vibe Coding with Steve and Gene on YouTube.

- About The Authors

Gene Kim

Gene Kim has been studying high-performing technology organizations since 1999. He was the founder and CTO of Tripwire, Inc., an enterprise security software company, where he served for 13 years. His books have sold over 1 million copies—he is the WSJ bestselling author of Wiring the Winning Organization, The Unicorn Project, and co-author of The Phoenix Project, The DevOps Handbook, and the Shingo Publication Award-winning Accelerate. Since 2014, he has been the organizer of DevOps Enterprise Summit (now Enterprise Technology Leadership Summit), studying the technology transformations of large, complex organizations.

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Steve Yegge

Steve Yegge is an American computer programmer and blogger known for writing about programming languages, productivity, and software culture for two decades. He has spent over thirty years in the industry, split evenly between dev and leadership roles, including nineteen years combined at Google and Amazon. Steve has written over a million lines of production code in a dozen languages, has helped build and launch many large production systems at big tech companies, has led multiple teams of up to 150 people, and has spent much of his career relentlessly focused on making himself and other developers faster and better. He is currently an Engineer at Sourcegraph working on AI coding assistants.

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