Why AI-assisted PRs merge at half the rate of human code | LinearB’s 2026 Benchmarks

**Dan Lines** (0:05)
Hey, what's up, everyone? Welcome back to Dev Interrupted. I'm your host, Dan Lines, LinearB, COO and co-founder. Today's episode will focus on the 2026 Engineering Benchmark Report from LinearB. So this year's report, it's our biggest, our most comprehensive yet. It's an amazing, amazing report. We're going to take a look at what the data says about engineering teams and really specifically how AI is fundamentally reshaping the way we build software. And to help me walk through all this data, I have my fellow co-host and LinearB's Director of AI Innovation, Ben Lloyd Pearson, BLP, to answer all of my questions about this year's report. Ben, always great to share an episode with you. Welcome to the show.

**Ben Lloyd Pearson** (1:03)
Yeah. Thanks for having me. It's always a lot of fun to just share all the cool research we're doing at LinearB. So a lot of cool data for us to unpack today.

**Dan Lines** (1:12)
Yeah. Amazing. And you and I were talking before the show. You came on Dev Interrupted to do this, I think, like two years ago, maybe even three years ago.

**Ben Lloyd Pearson** (1:23)
Three years ago.

**Dan Lines** (1:24)
Three years ago. And so I think it will be pretty interesting to see how this has progressed. Obviously, a lot of it has to do with AI. But how do you feel about now coming on three years later and doing the same thing but with an updated report?

**Ben Lloyd Pearson** (1:42)
Oh, man. I mean, it's wild to think about just how different the world is today versus when I first started here at LinearB. I mean, AI was barely even something we were talking about at that point. And now it's like all we can talk about. And I feel like everything I'm doing is now being impacted by it. So, you know, of course, so is this report. And there's going to be a lot of new AI stuff that we're going to talk about today, which is pretty awesome.

**Dan Lines** (2:04)
Yeah, it's really cool. I mean, I read through everything in there and definitely like it's going to be exciting to get into the AI stuff that's really popping out. A lot of good insights there. But I guess probably the best way. Let's start with an overview. Let's start out with an overview summary. Maybe you can give us an overview of what makes either this report unique or maybe some of the things that are kind of like jumping out at you for this year.

**Ben Lloyd Pearson** (2:33)
Yeah. Well, like you said, it's the most comprehensive analysis we've ever done. 8.1 million poll requests, about 4,800 engineering teams spread across 42 different countries. So pretty large scale of data that we're dealing with here. And what really sets this year apart from is that we've introduced a completely new dimension to this report around AI productivity and specifically how to measure it. So for the first time, we're not just looking at all the traditional software delivery metrics. You know, those are still there. We still reporting on them. We're also examining how AI is impacting engineering workflows across the board. So and on top of that, we've combined for the first time ever qualitative data along with our, or excuse me, qualitative analysis, along with our quantitative data, where we surveyed a bunch of engineering leadership or engineering leaders about how they're using AI within their organization. Both to understand the data behind what's changing, but then also the perception of the leaders that are running these things. And if I had to pick one thing that is sort of the top line that everyone should take away from this, is that AI adoption is effectively maximized at this point. It's almost universal. So in our findings, 88.3% of developers now use AI regularly. So that's, you know, at least multiple times a week, which is up from 70, just under 72% when we last surveyed this back in early 2024 But I want to make, I want to stress one really critical point, and that is that adoption does not equal impact.
So AI-generated PRs are behaving completely different than ones that are unassisted by AI. They're larger, they wait longer for reviews, and they merge at less than half the rate of human-authored code. So what I really want people to take away is that AI is accelerating code generation. I think we all know that at this point. But it's also exposing bottlenecks everywhere else in the SDLC, primarily with things like reviews, testing, governance, organizational readiness. There's still a lot that needs to be solved outside of code generation.

**Dan Lines** (4:46)