Leading Dev Teams Past the AI Velocity Trap

In my last post, I talked about the macro risks of the sudden AI transition, including the economic dominoes, the Q1 budget burns, and security moving at breakneck speed. But once you accept those risks, you’re left with a more immediate operational reality:

The macro risks matter, but the day-to-day reality is where leaders are getting crushed.

What does it actually feel like to lead an engineering team when the old playbook has been set on fire?

It feels like relentless, non-stop pressure to keep the ball moving forward.

Marc Andreessen recently described the idea of the “AI Vampire,” and it perfectly captures what’s happening inside engineering teams right now. AI hasn’t reduced our workload; it has stripped away the natural, human-paced pauses we used to rely on.

In the past, writing software had built-in breathing room. You wrote code line by line, waited for builds, or spent days whiteboarding a problem. That structural framework gave developers time to process their work.

AI completely drains that framework. When an assistant can generate an entire architectural layout or hundreds of lines of code in three seconds, the downtime vanishes. Our workload hasn’t shrunk; it has shifted from a slow creative process to a non-stop game of cognitive triage. We are no longer just writing code, we are managing a relentless firehose of machine-generated execution.

And that velocity is creating a generational and experiential divide inside engineering teams.

The Two Velocities: Juniors Overwhelmed, Veterans Overcautious

Early-career developers are feeling the strain of this pace. In industry circles, you hear a lot of anxiety from junior talent who feel like the organization is simply moving too fast. When velocity explodes, my perspective is simple: you don’t slow down to catch your bearings, you accelerate into the curve.

We have to combat AI with more AI.

That doesn’t mean letting the machine run on autopilot. Doing things blindly is NEVER the answer, and human intervention remains 100% necessary. Instead, it means teaching developers to strategically lean on AI to build out robust test suites, generate boilerplate, and clear away early development hurdles. We use the tools to compress the timeline so we can reach the first mile marker of the plan as fast as possible, while keeping our engineering judgment firmly in the driver’s seat.

On the other side of the aisle, veteran developers, who have spent decades watching rushed architecture collapse, are looking at this toolset with deep caution. Their instinct is to pull back on the reins.

Across the industry, leaders are experiencing back-to-back 1:1s where one perspective says “this is too fast” and the next says “we’re moving recklessly.” That’s the classic velocity divide.

So the leadership challenge becomes a balancing act:

  • Help less experienced devs manage the anxiety of pure velocity
  • Encourage senior devs to loosen their grip and trust the tools enough to lean in

Both groups are right. Both groups are wrong. And both groups need different coaching to operate effectively in this new environment.

The Tooling Crisis: When Agile Breaks Under AI

This level of velocity creates an immediate crisis for engineering workflows. Traditional Agile, including rigid two-week sprints, meticulously groomed Jira tickets, and predictable scope, is buckling under the pressure.

These systems were built for a world where humans wrote code line-by-line and where project managers could forecast work over a fortnight. That world is gone.

When a developer using AI can blast through a week’s worth of sprint tickets by Tuesday afternoon, the process becomes an administrative bottleneck.

Many leadership teams are experimenting with Kanban to better visualize the work dynamically. But Kanban introduces its own challenges when the volume of tasks is exploding. Tracking a fluid, hyper-accelerated pipeline without losing control of the architectural narrative is a constant battle.

The truth is uncomfortable but clear: AI breaks any workflow designed around human-bounded throughput. We’re all rebuilding the plane while flying it.

Orchestrating Multiple Orchestras

How do you keep a team aligned when the ground is constantly shifting?

It requires a fundamental shift in leadership strategy. Every developer must own a highly distinct, hyper-defined piece of the master plan. There is no room for passive passengers or developers waiting to be handed a ticket. Everyone must know exactly what section of the architecture they are responsible for executing.

Crucially, velocity cannot come at the expense of safety. To handle this speed, it helps to enforce a rigorous collective review process for all AI-generated plans and code, ensuring human eyes audit the machine. Gating the foundation to keep it secure, such as ensuring specific database experts review all database-related code before it touches production, is essential.

The leadership role transforms. It’s less about task management or code review and more like conducting multiple orchestras simultaneously.

You have:

  • The backend orchestra playing at maximum volume
  • The AI-integration section moving at lightning speed
  • The data-reasoning agents stitching everything together in the corner

If they aren’t synchronized, the result isn’t music, it’s chaos.

Survival in this new era requires disciplined organization and constant communication. Without stepping back, it’s impossible to see the forest through the trees and teams end up reacting instead of executing. Leaders have to zoom out, understand the whole system, and make sure the velocity we’ve unlocked is moving the architecture forward, not fracturing it.

To the Engineering Leaders Reading This

This cultural divide, with juniors overwhelmed by speed and seniors cautious from experience, is becoming one of the defining leadership challenges of the AI era.

How are you managing it inside your teams? I’d love to hear what you’re seeing in the field.