Why enterprise AI lives or dies on applied research | Contextual AI’s Elizabeth Lingg

**Andrew Zigler** (0:06)
Welcome to Dev Interrupted. I'm your host, Andrew Zigler.

**Ben Lloyd Pearson** (0:10)
And I'm your host, Ben Lloyd Pearson.

**Andrew Zigler** (0:12)
We're here on site at the Engineering Leadership Conference here in San Francisco, meeting with over 120 engineering leaders, including our friends at Expedia, showcasing their Quads framework that they built on the Linear B platform.

**Ben Lloyd Pearson** (0:26)
We'll have that episode coming out here in a few weeks for all of our listeners. It's a really great one, I think. We've been sitting down with a lot of engineering leaders here in the dome at this event. A lot of episodes getting recorded that you all are going to hear in the upcoming weeks, so stay tuned. Lots of good content upcoming. Been a really great event, Andrew, and I really feel like the whole tone of this event was set. The very first morning, the first conversation I had, I had these two engineering managers walked up, had barely finished my first cup of coffee, and had a really wonderful conversation with them. Naturally, in 2025, anytime you talk to somebody in tech, it always ends up coming down to AI. Eventually, you're going to start talking about AI. And they told me they've done what everyone's doing these days. They've adopted all the tools. They've got Copilot, they've got Cursor, Claude Code, Windsurf. They had a laundry list, and they just kind of listed off. And I was like, well, you guys are doing it all. And they even told me they had bought some dashboard from one of the vendors here to measure it all. And I was paying a lot of money for it. I got all these pretty visualizations. But they were like almost distraught over it. And they're like, there's this AI everywhere. Like we have it all over our booth even, but it's all over all the booths. The event itself makes AI really a central component of the entire experience. And they were kind of distraught about how they had all these tools, they had all these metrics, these dashboards, but they still felt stuck. They weren't actually sure if they were making the right decisions and if productivity was getting better and if their developers were actually being more effective, being more efficient, producing higher quality. In fact, they were actually starting to experience some backlash from some of their developers. And particularly over one of the metrics that their executives had started to really get stuck on was PR throughput. Like that was, they saw that dashboard and they were like, oh, can we make that number go up?
And naturally the developers are like, why do we need more PRs? Like, is that really the thing that we should incentivize? And even the engineering managers are, they're a little skeptical of that metric as well. And personally me, I immediately just sort of cringed a little bit because I once worked on an engineering team that we had two North Star metrics. It was the number of commits that we created and the number of lines of code that we changed. So naturally we just wrote some scripts to make those two metrics go up. And sure enough, these two engineering managers, they even described a recent conversation they had with their engineering team where it was getting a little fraught and the engineers were wondering why they were so focused on these metrics. And the engineering manager just had to say, look, if our executives are so focused on this one metric, PR throughput, and you have these AI tools, you guys know how to make that metric. You can use AI to make more PRs. Look, you can just do it. I know it's not the thing that's going to be best for all of us, but you can just do it. There's nothing stopping you. I felt so bad for them, honestly. That's not what you want to do to your team. That's not the kind of relationship you want to have.
It's not healthy. That's not going to be productive. But it's a common refrain we hear. A lot of these organizations, they really never make it past that measurement phase of the developer productivity journey. They buy this dashboard, they get a framework, and the execs might think that's enough, but the reality is that you've really just barely even made the first step at that point. If you don't take action, if you don't realize, if you don't take any steps to improve upon the things that you're measuring, then you're never going to realize the full potential of AI. I think that's a theme that we're seeing time and time again here, not only at this event, but it is coming up a lot here and elsewhere. And we're obviously here with Linear B. We have the Dev Interrupted Dome. Linear B is also here with Airbooth, showing off a lot of the cool new features. But what I really love is that, particularly with Linear B is they're not here to just show you how to measure AI and be done with it. They're really focused on how to get better with AI. So as a part of this, Linear B has announced a bunch of new features around it. So we've got the AI code reviews that are doing really awesome, especially in head-to-head comparisons against all the tools that are out there, like Copilot. Because you think about code reviews, one of the most common bottlenecks in the SDLC. So really giving practical improvements to Dev teams. We also, they announced the new MCP server that just generates helpful artifacts to help you make decisions. It's a new way to consume data within teams.