When Meta’s senior engineers couldn’t explain ‘vibe-coding,’ we knew something broke. Traditional leadership tools failed, and we had to evolve. Learn how, at Meta, we changed how we hire, evaluate, and lead.
A while back, during one of Meta’s new hire orientations, a senior manager froze when new hires asked him about “vibe-coding.” He nodded politely but clearly had no idea what they meant. That moment exposed more than a generational gap, it was a warning; The gap between how fast technology evolves and how fast leaders adapt is widening, and we must do something about it. ASAP.
Junior engineers now arrive with AI superpowers. The competitive advantage shifted from raw technical skill to adaptation speed, from code quality to communication, and from knowing frameworks to catching AI hallucinations. This forced Meta to rethink how we hire, evaluate, and lead.
We built AI-native coding interviews that measure adaptation, not memorization. We learned to evaluate talent without the traditional technical signals, and shifted the skills we look for when hiring, promoting, and assessing performance.
We also changed how we lead: leaders have to use AI themselves before teaching their teams. They learn to adapt , rather than using specific tools, so they can evaluate whatever shows up next. They needed to learn how to leverage AI without the price of dependency. We realized AI accelerates junior engineers faster than any technology we’ve seen, but despite the panic about job losses, AI can help ambitious engineers break in and move up, but only if their leaders understand how to guide them.
In this talk, I’ll share what we learned, and the frameworks we developed. I’ll share the mistakes we made, and what actually works when there’s no playbook. You’ll leave knowing what skills matter now, how to evaluate talent differently, and how to lead when yesterday’s answers don’t apply anymore.
Key takeaways
- AI-native coding interviews: How we rebuilt coding interviews to evaluate adaptation and not memorization.
- New evaluation criteria: What skills actually predict engineering success when AI changes the competitive advantage
- Leadership frameworks for AI adoption: How to train leaders to use AI themselves and guide teams without creating dependency
- Data-backed insights on junior engineers: Why AI accelerates junior engineers faster than any previous technology and how to leverage that effectively despite job loss fears
