# The engineering productivity gap: How elite AI teams are pulling away from the rest

> Join a live workshop on the new benchmark data behind the engineering productivity gap, and the measurement play that puts your team on the right side of it.

[](https://linearb.io/ "LinearB")

[Register now](https://linearb.io/event/engineering-productivity-gap#event-form)

Virtual Webinar

# The engineering productivity gap: How elite AI teams are pulling away from the rest

Join a live workshop on the new benchmark data behind the engineering productivity gap, and the measurement play that puts your team on the right side of it.

## Session

10am PT on July, 30, 2026

## Speakers

![Photo of Andrew Zigler](https://assets.linearb.io/image/upload/c_fit,w_3840,h_3840/f_auto/q_auto/v1/andrew_zigler_headshot_a1184de4a5?_a=BAVMn6ID0)

### Andrew Zigler

GTM Engineer, LinearB

![Photo of Ben Lloyd Pearson](https://assets.linearb.io/image/upload/c_fit,w_1080,h_1080/f_auto/q_auto/v1/ben-lloyd-pearson-04-2025?_a=BAVMn6ID0)

### Ben Lloyd Pearson

Senior Director AI & Product Marketing, LinearB

## About the webinar

The distance between engineering teams that have operationalized AI and the teams that haven't is no longer a rounding error. The gap is real, it's measurable, and it's getting wider every quarter. If your org hasn't yet turned AI adoption into delivered work, you're already falling behind the teams that have. Adoption on its own doesn't close the gap. If you measure AI-generated code the way you measure human code, it can even look like a problem, with bigger PRs and slower reviews. What separates elite teams is leverage, the ability to turn AI activity into merged, shipped work. Capturing it means seeing where AI is working, where adoption is stalling, and where you can replicate what your strongest teams already do. In this session, Andrew Zigler and Ben Lloyd Pearson walk through a mid-year benchmark refresh built on 2.7 million pull requests from 253 engineering organizations. You'll see how far the AI-elite teams have pulled ahead, why high AI usage correlates with a 1.7 to 2.2x lift in PR merge rate (roughly 1.5x for a typical team), and where that leverage leaks back out of the pipeline. You'll leave with the measurement play the strongest teams use to close the gap on purpose instead of by luck. Register to join live and get first access to the full mid-year benchmark report the moment it's released.

What you'll learn

Where elite adoption actually sits today, and why there's far more runway than most leaders assume

How high AI usage correlates with a 1.7 to 2.2x lift in PR merge rate, and what pulled elite teams away from the pack almost overnight

Why more AI code doesn't automatically mean more delivered code, and the ownership gap behind agentic PRs that stall in the pipeline

The lowest-effort velocity win available now, turning on AI code review for up to a 5% lift in merged PRs

The measurement play for closing the gap on purpose, and how to replicate your strongest teams instead of running on gut feel