How AI is Changing Engineering Management

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We recently introduced monthly themes in the Refactoring Community, as a way to work all together on one topic at a time, and to get deeper and more interesting insights.

In October we discussed Management in the Age of AI: we had a big community thread, an AI Club edition, a Mastermind, and last week we asked everyone a simple question:

In two years, will your company need more managers or fewer?

Outside the community, the answer many CEOs are giving is fewer:

• Jack Dorsey wrote that “there is no need for a permanent middle management layer“ at Block.

• Brian Armstrong announced “no pure managers“ at Coinbase – every leader must also be an active individual contributor.

Also, it seems that most conversations about AI are about software factories, bottlenecks, code reviews, and all the IC stuff. Very few are about what happens to the people who manage those ICs.

So, today we look at what AI is really changing in the manager job, using a month of community conversations plus a ton of research and recent stories. Here is the agenda:

• 🎯 What a manager is for — four areas that haven’t moved.

• 🧹 What AI takes off your plate — glue work, status, and some building.

• ⚔️ The AI giveth and the AI taketh — more review, slop, and the AI transition itself.

• 🧭 How to set the quality bar — slop as feedback, measuring what matters, reviewing the harness.

• 🛠️ What to do with the space that’s left — a small model for using the time AI frees up.

• 📚 Resources — 10+ articles and stories to help you dive deeper.

Let’s dive in!

🎯 What a manager is for

To see what AI changes, we have to start from what the job is for, and that part to me hasn’t moved a lot. Andy Grove wrote in High Output Management that:

I believe that’s still pretty much it.

On top of this, everything a manager does to raise that output falls more or less into four areas: Product, Tech, Process, and Team.

These are the same four pillars I used back in 2024 to describe what an EM does, taking a page from Pat Kua’s EM archetypes, and I believe they still hold. Each of them answers a different question about the team’s output: what we build, how we build it, how the work flows, and who does it:

1) Product 🧭

Shape what the team builds and why, together with PMs and stakeholders, and keep the technical direction aligned with what the business needs.

2) Tech 🔨

Make sure the thing is built well: design, quality, reliability, and the technical risk that comes with them. This is where technical skills still matter for managers, because it is hard to judge what good looks like if you have never built the thing yourself.

3) Process 🚚

Make sure the work gets done in a reasonable time, without too much friction: planning, scoping, coordination, moving information around, and removing obstacles.

4) Team 🪴

Enable trust, alignment, and growth across people and teams, from hiring to careers. This is the human core of the job, and probably the part that has changed the least.

No manager covers all four equally, of course: how you split your time depends on the company, its stage, and especially on where your team is strong already. Nevertheless, AI touches all four of these, both in good and bad ways.

Let’s start with what it takes away.

🧹 What AI takes off your plate

The first obvious area where AI is helping managers is glue work, like moving information between tools, writing up meeting notes, and in general, processing information coming from disparate sources.

Anyone who has worked as a manager for a long time knows this is 1) incredibly time-consuming, and yet 2) absolutely foundational to the success of your job.

Fetching status is an easy example:...

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