AI Is Making Engineering Management More Human

“Half the job got faster and half didn’t move at all.”

That’s how Tasha Ramesh, an Engineering Manager at Tinder, described managing engineers a year and a half into the AI shift. The operational side of the job, synthesizing information, surfacing blockers, tracking projects, has sped up. The human side, coaching engineers, giving hard feedback, resolving disagreements, hasn’t gotten any easier. If anything, it matters more.

Much of the industry has drawn a different conclusion. Companies are flattening organizations and asking managers to oversee larger teams. Meta’s Applied AI org runs roughly 50 engineers per manager. The implicit bet is that more capable engineers need less management.

That skips the more useful question: if engineers are becoming more capable, what kind of management do they need now?

We spoke with engineering leaders at Anthropic, Magnite, and Tinder, where I’m CTO, about what’s changing inside their teams. Four shifts stood out.

1. Managers need to become player-coaches

“Don’t track output. Track judgment.” — Aaron Wang, Engineering Manager at Anthropic

Now that engineers can generate code and explore solutions faster, sheer output tells managers less than it used to. Managers need to understand the thinking behind the work. What possibilities did you consider, and why did you choose this one? What trade-offs did you make? Why is this the right solution?

Managers can’t develop that judgment from the sidelines, which is why so many are taking on the role of player-coach. The best ones are building with AI themselves, testing the latest tools and forming their own view of what good AI-assisted engineering looks like.

2. Give engineers bigger problems and make them own the answers

“Managing senior engineers increasingly feels like managing engineering managers.” — Ryan Burns, Engineering Manager at Tinder