AI Managers: More Realistic Than Replacing Engineers

Why Are We Trying to Replace Engineers, Not Management?

The promise of AI in tech is seductive: type a prompt, get an app. Skip the engineers, cut the cost, scale the dream. VC decks are glowing with slides about 10x productivity, 1-person unicorns, and engineering headcount slashed to the bone.

But if AI is so powerful—so capable of replacing human effort—why are we only aiming it at the people who build?

Why aren’t we trying to replace management?

The Asymmetry of Automation

From Copilot to GPT-4, the direction of AI tooling is clear: remove friction for developers, and eventually, remove the developers themselves. The logic is economic. Engineers are expensive. They’re in high demand. They slow things down by insisting on quality, tests, and feasibility.

But in the same companies that talk about AI replacing code, org charts continue to swell with new layers of product managers, team leads, delivery managers, and middle managers managing other middle managers. If we’re trying to streamline how companies build software, shouldn’t we be scrutinizing all functions?

It’s not like management is immune to automation. In fact, it’s uniquely suited to it.

AI Can Automate More Than Code

A huge portion of management involves:

Monitoring KPIs and metrics

Sending out reminders and reports

Translating between teams

Writing status updates

Scheduling meetings

Generating performance reviews

These aren’t mysterious, human-only tasks. They’re repeatable, trackable, and data-driven. They’re exactly the kind of patterns large language models thrive on. You could build an AI to coordinate a sprint and write the retro summary today—and it would probably do it more consistently than most team leads.

In fact, many of these tasks already have AI-powered tools. They just don’t get packaged as “management replacements.” Why? Because…

Management Is Closer to Power, Not the Product

There’s a quiet reason we don’t talk about automating management: it threatens the structure.

Management is how power is distributed. It decides how budgets are spent, who gets promoted, and what gets built. When you automate a dev, you reduce cost. When you automate a manager, you shift control. And the people who would greenlight that change… are managers.

There’s also cultural inertia. In many companies, being “promoted” means moving away from the work and toward coordination. If AI could coordinate better, what happens to that ladder?

This isn’t an argument against all management. Good management creates clarity, alignment, and psychological safety. But let’s be real—most of what passes for management is reporting, planning, and shielding people from other managers.

And AI? It’s really good at that.

What If We Used AI to Flatten, Not Just Accelerate?

Imagine an org where:

Engineers use AI to write, test, and deploy faster

Project plans are generated and updated automatically

Performance feedback is AI-assisted, but human-led

Roadmaps emerge from product usage data, not HiPPOs

Middle layers are removed—not to cut costs, but to increase autonomy

AI shouldn’t just be used to extract more from fewer engineers. It should be used to decentralize and empower them. To remove unnecessary blockers, not create new ones.

Maybe what we need isn’t fewer builders. Maybe we need fewer bottlenecks.

The Real Question

The tech industry keeps asking:

“Can AI replace engineers?”

Maybe it’s time we asked:

“Should it replace management first?”

Not because engineers are irreplaceable. But because real innovation doesn’t come from replacing the people doing the work. It comes from replacing the systems that prevent them from doing it well.