The playbook to close your team’s AI productivity gap | LinearB’s Yishai Beeri

0:00Yishai Beeri:Welcome back to Dev Interrupted, brought to you by Linear B. My guest today is Yishai Beeri, Linear B's CTO, and he spent the last few months exploring at Linear B whether this AI spend in the industry is turning into measured, delivered work. Because something shifted in the last year for engineering teams, and we're all still grappling with it. Some teams pulled ahead with AI, others stayed flat, and some even took a dive. And left unchecked, that gap can rip your engineering team in half. So in today's episode, Ben and I dig into how to close that gap, improve AI ROI. Here's our conversation with Yishai.

0:37Yishai Beeri:AI has changed how software gets built faster than any single year of our benchmarks could possibly capture. So this month, Linear B is publishing a mid-year refresh, built on a fresh data set of millions of pull requests from hundreds of engineering organizations from February to May 2026. We've been digging really into AI data within this data set, you know, AI usage where it's being adopted and leveraged. And the results have been striking and very clear, as a matter of fact. And the fact is that there's a gap that's opening between teams that are getting significant leverage out of AI and the teams that have merely just switched on or maybe not even really adopted it yet.

1:19Yishai Beeri:And that gap is widening at an extremely rapid pace. So we wanted to bring our guest on today because he has a very clear picture of all of this data that we've been collecting and what it means for engineering leaders. So I want to welcome Linear B's CTO, Yishai Biri. Yishai, welcome to Dev Interrupted. Welcome back.

1:39Dev Interrupted Hosts:Thank you. Yeah, great being here again. Great to have you here.

1:44Yishai Beeri:Yeah, so what we're really focusing on is, you know, how there's been this significant shift that we have now seen starting in January. of this year. And everyone has started to pay attention to like AI costs and like what they're getting out of it. And we're in this critical moment right now, like at this point in time, where we are beginning to see teams that are effectively investing into AI and getting significant improvements, productivity improvements out of it. So we normally do our sort of engineering benchmarks once a year, sort of at the start of the year to kick everything off. But we felt that things were changing so fast that we had to start covering this on a more regular cadence.

2:28Yishai Beeri:So Yishai, I just want to start there. Like if we weren't looking at this data here in the middle of 2026, what do you think we would be missing? Like, what are we getting out of doing this, this refresh of our benchmarks data halfway through the year?

2:40Dev Interrupted Hosts:I think two things at play here. One is behaviors are beginning to change rapidly across the developer organizations that we're looking at. So waiting a full year to get a fresh grasp of what are the behaviors, how do the common metrics look like, that's too slow. Changes are so fast, you want to make sure that your benchmarks are actually capturing what's going on now and not what happened six or nine months ago. The other thing is a focus on some new benchmarks around AI, how it's getting used, the cost because the focus in the market around these questions has really increased. And if a year ago or even six months ago, people were asking about adoption, now it's about cost and ROI.

3:33Dev Interrupted Hosts:This has quickly become the number one priority. I'm speaking with dozens of engineer leaders and everyone is saying the costs are growing and we're still, they're not finished growing. We all know that. I need to show actual ROI. This is now CFO, CEO level visibility. I need to show that this token spend is actually giving back hard business value and it's worth it. It's worth the hundreds and sometimes thousands of dollars a month for a developer. That focus means that we have to be talking and showing metrics and benchmarks and data about how AI is being used. And is it getting the or creating the right leverage, the right productivity benefits to actually justify all of this cost?

4:21Dev Interrupted Hosts:And then there was this, there's always been hype, right? People reading about my dev team is 2x, 5x, 10x companies saying I can cut half of my workforce because of AI. All of this hype now has to meet reality. So mapping, are we there yet? Is the hype actual, like, is it real? What are the gaps and how is that meeting a specific developer organization or the market at large? That's what we set out to do here. And this cannot wait to an annual cadence of refreshing benchmarks.

4:57Yishai Beeri:Yeah, I think it's really smart to point out that if you were to wait a year, you'd be missing so many learnings, patterns, behaviors, and honestly stumbling for that year. Because the reality is that the adoption of these tools, the impacts of them are in more real time than they've ever been. These rollouts are more aggressive. Folks are building more things more quickly. and just the need for more real-time awareness over what these tools are doing in your organization and what you're getting out of them is just, it's never been higher. And it really does tie to the whole story of cost, like you were saying a moment ago, because now we're working with our technology almost on the same kind of cadence as how financial leaders think about how they're going to be thinking of their budgets.

5:40Yishai Beeri:Like on a quarterly basis, how are we justifying? What did we do last quarter? What are we doing this quarter? Now our tools and our AI spend are right in those conversations. And so we need just as much real-time data that justify the things that we're doing with those tools and understand the downstream impact. And like finance, by the way, is like really in the room on these adoption conversations. You know, folks that are leading these budgets at larger organizations are really, really obsessed with the ROI question. They're coming to their technology leaders, many of whom are listening to our show.

6:13Yishai Beeri:And they ask them, like, prove it. Prove your AI rollout is working. And that's where, you know, folks turn to things like metrics and how their tools are getting adopted to understand how it's moving through their org. and maybe you could shine some light on us for this ROI question. And it's so predominant. Like you mentioned it even a moment ago that it's all around cost. You know, why has this become such a number one lens? And, you know, why does that push us to benchmark what productivity means? How does that help that conversation? So I think finance is not just in the room.

6:47Dev Interrupted Hosts:They came in late. They kick the door in, frantically trying to get on top of what's happening. I've heard this from more than one of our customers where, you know, first, let's just go all in on AI. Token maxing, experiment. We can't miss the boat. Have everyone use it. Let's see what happens. And let's be pretty honest. All of us are still experimenting. A lot of it is maturing, but there's no mature model for using AI in a dev work yet. it's all very, very experimental. Everyone has to play, has to try the new shiny things because the impact can be so great. And now is the cycle where finance is starting to see, okay, the budget we had placed, it was earmarked because no one knew how much is it actually going to cost.

7:38Dev Interrupted Hosts:And these budgets were blown away in two or three months, the whole budget for the year. There was a management offsite and we came back with a clear mandate. We have to show ROI for the spend. Not because we're going to cut it tomorrow, but because it's not large enough to show up on the CFO's radar. It's no longer a side thing. It's becoming, I don't know, 5 % of my dev spend, 10 % of my dev spend, maybe 20 % in some places, which is substantial. It's now becoming a real thing. And combine that with the price hikes and, you know, There was a lot of recent activity in the last three months with models exposing more of the actual cost to customers.

8:22Dev Interrupted Hosts:I think we all know we're still heavily subsidized when coding with AI in most of the footprints. So there's still room to grow for those prices to grow. We're not paying the actual inference cost yet. And that dawned on those organizations. We have to start looking at not just spending whatever we can to get on top of the technology, but also how do I start introducing some kind of structure and process to my spend decisions and my spend budgets? I think these just culminated now in a very sharp focus on how much is it actually costing me? Can I attribute these costs to specific projects or types of work or teams and so on?

9:11Dev Interrupted Hosts:And I don't know, the golden, the holy grail, what is the ROI? What is it giving me back? Even when all my developers are saying, this is great, I need something more concrete. I need to show this is actually helping me move so much faster.

9:26Yishai Beeri:I feel like the story of last year was like, are people actually using AI? Like, as you mentioned, and I feel like that question was sort of like put to rest basically at the end of last year. It's like every study that was looking at this, including our own data, when we looked into this was saying that like basically 90 plus percent of developers have adopted AI in some manner within their daily or weekly workflow. And, you know, so that's like no longer the thing that people question anymore. It's like it just sort of assumes that AI is now ubiquitous across the organization. And it's not really a thing that actually indicates whether or not you're seeing productivity gains out of it.

10:09Yishai Beeri:Really, what it comes down to is how much leverage you're getting from it, like how much how much of AI's outputs are actually showing up in things that provide value to your customers, you know, or that get shipped into production. We've been sort of cautious at LinearBeat to like fully lean into like just measuring adoption because we understand that it's very limited in terms of like what it can actually tell you even at the most like ai forward uh companies explain this like leverage challenge to me like why why is it you know now that we've moved beyond adoption metrics like um what is it now that we need to be looking at to really understand where ai is impacting us yeah so

10:49Dev Interrupted Hosts:when you assume and that's typically a correct assumption that your developers are using ai in some shape in their daily work or in their work, typically as coders or people bringing new code to the code base and creating value to the company through that. You're asking, okay, what are the returns? And there's many, many factors that will influence what I'm actually getting from AI or from that new tool that I'm paying for in the hands of my developers. So this could be about, yeah, the same task, I can write the code much faster or Claude or whatever AI tool I use can write the code for me much faster.

11:31Dev Interrupted Hosts:But coding is just a small part of what developers do. And coding is not enough to deliver value. So you have to look at how is AI helping me move faster? and there's a plethora of behaviors and changes in how the sdlc works with ai that will impact or you know limit or maybe unblock the kind of value i'm getting from ai let me give you an example if ai helps me write code faster by that code is sloppy and i will need to rework it in two weeks because of runtime bugs or because of production failures, then I did not really improve my velocity, right? I'm not delivering that much more because now I'm stuck fixing those issues with much more expensive cycles.

12:18Dev Interrupted Hosts:So the impact on quality, on the ability to make the second or third change in that code base, all of these compound into the overall productivity question. Maybe I can get the code written fast, but I can't review it fast enough. I have a new bottleneck with reviews and acceptance that will limit the productivity gains I can get from AI. So yeah, I code 10x time faster, but I'm not delivering, and certainly the team is not delivering 10x more if they're stuck on a new bottleneck. Not to mention some software things like, are my seniors getting stressed out by having to just review crazy AI PRs all day?

13:04Dev Interrupted Hosts:Like, are they burning out on a new type of load and new type of focus in their work? Because I have to get, you know, people have to review code, at least in some places. And that code is now written by AI, which is harder to review. It's typically larger, but the PRs are larger. A lot of factors. And then that becomes a problem with my talent over time. am I growing seniors? Well, next year will I have seniors? What is the path from junior to senior in an AI-dominant SDLC? So there's this either directly impacting productivity items or softer ones, which will take a little more time to compound.

13:50Dev Interrupted Hosts:But almost like a back-to-basics, there's many moving parts in developer productivity, and all of them will affect the kind of value I can get from AI. So I may be investing in tokens or getting my people the best tools, but if I'm not solving for these problems, that will diminish or even erase those productivity gains I can get from AI. And when we talk about leverage, it's basically saying, how efficient are you in translating the AI investment and all the goodness that AI and the SOC can give me into actual productivity gains. Yeah. And like you hinted at the beginning, we're seeing a very big divide in that kind of productivity gains that different teams and different organizations and even different developers get.

14:39Dev Interrupted Hosts:And I think that's where it becomes interesting. What sets apart the people or the organizations that are able to get those 2x, 3x more productivity gains compared to the ones that are, okay, I'm getting 10%. And what should I change to get closer to that high end of leverage?

15:00Yishai Beeri:Yeah, and I think this is a good place to just inject some of the specific data from this report, because I think it's really relevant to this moment. And we've kind of buried the lead a little bit on this, but I think everything we've discussed at this point is really important to context for it. But what we've seen is that developers who are in the highest category of AI usage, and the way we came up with that number is if they're in the P90s, the 90th percentile of developers who are using AI for code that goes into a pull request. It's how we're looking at this. Year over year, they've more than doubled their output in terms of code that has been merged into production versus people who really don't use AI for the code that goes into their PRs.

15:45Yishai Beeri:They're basically flat year over year. Today looks just like it did a year ago. And that's that gap that we're talking about. And it correlates pretty well all the way down the cohort. So P75 is also seeing an uplift, but not nearly as much as that top tier group. And that's really sort of the headline takeaway that we got from this report is that that is what has changed since January. These higher groups, these higher cohorts of AI users that are using it for productive work have more than doubled their velocity over the past year, which is a very significant change.

16:23Dev Interrupted Hosts:Yeah. So I think several things here. One is, I think, a renewed focus on a core metric, which is how many PRs can I get merged and normalize that by the number of developers. So a typical developer on my team, how many PRs can get it? Can they merge every week? So that helps. It's obviously like every metric is going to be a simplification of reality. But that capture is a good sense of the productivity. And if that number grows over time, this means, A, I have unblocked barriers to getting PRs merged. And that's like code delivered to my code base. And that's the business value behind that. And the growth there means I was able to translate AI to not just writing more code, but to actually delivering more code.

17:15Dev Interrupted Hosts:There's many ways you can argue about why count PRs. and I can split my PRs to get a better count. I can make my PR smaller to get a better count. All good things. I would urge you to do that. And I'm fine with that tack on the gap between model and reality in this metric because the ways to cheat this metric are actually ways to win. But maybe even more than cycle time, which for many years was the staple of our reproductive short cycles, what we're seeing with AI is typically same cycles, but more than parallel. So with the AI now being in the focus, our approach here is not to try and look at, are my cycles much shorter?

18:02In reality, they're not much shorter, but rather, am I able to deliver more?

18:07Dev Interrupted Hosts:And that happens through more parallel production by the developers as they're using AI to get more things done. So PR is merged by developer, like per developer per week. That's the metric we are looking into. Like you said, if we are comparing developers to a year back, we're seeing a wide range of multiples from the ones that have not changed anything and they're not using AI. So that's a good kind of control group for us. Yeah, nothing changed. You're not using AI really. And I'm not surprised that nothing changed for you. and then the ones at the top, which are able to do two or two, even two and a half X of their previous throughput.

18:48Dev Interrupted Hosts:So that kind of gives an idea of the range. It's not the maximum possible, but if you are a typical developer organization, we typically look at larger ones with hundreds of developers or more and you fall anywhere there, you're in the normal range. and now your question is am I getting 10 20 30 percent more than I used to do a year ago I'm in the low low ranges of AI leverage and I should be looking to get more if I'm in the 2x or two and a half x I'm at the you know pretty much at the top I can always get better but I'm in a good place and when we look at the graph and we will obviously be sharing that you know the the full data, you see a big jump in January onwards.

19:35Dev Interrupted Hosts:So from last June to January, things are pretty much stable with a low range of leverage numbers, if you like, or increase in PR throughput. And then in January, things start to change. And now the leaders begin to grow, break away from the others. I think it's obvious that there were like key model or frontier model drops that made this a reality, along with ongoing improvements in the harnesses and the tooling around this. So for the industry, something clicked around January of this year. And that something is now finally translating into actual productivity gains. It's not the 5x hype. And even 2x is a broad, like it's not the average.

20:24Dev Interrupted Hosts:2x is good um but 2x is now achievable and the the devs that get it the teams that get it and the ones that are able to remove the other bottlenecks are now getting that 2x 2.5x etc

20:37Yishai Beeri:i think it's really smart that you called out that adoption hides things you know if you just look at adoption you're going to miss all these other patterns and behaviors especially even going back to what you were saying about rework like if you're just tracking token spend you're just tracking the usage of the tokens, then it's completely hiding how much rework, refactor, fixing and cleaning up of messes that AI is making is actually happening. And in fact, it makes it look like more good things are happening. So it misdirects the narrative. And also, there's a really fascinating thing that happened right around the end of last year, which you touched on, where folks went on, you know, they went on holiday break, they had some time to themselves, the models were getting kind of good.

21:20Yishai Beeri:And then they came back at the top of the year and maybe they had a hobby project that they did and they shared or they otherwise were able to experiment without just having the week-to-week race of getting their PRs done. And they were able to do a reset at the top of the year. Oh, this is how I work. This is how I structure the beginning of my tasks. This is how I do multiple things in parallel, which is, I think, the key to getting more of these impacts to tracking like, oh, these folks that are getting 2x, 3x, however many x more out of their output, that's the real secret to them. It's not that they're taking shortcuts or that all of their windows are super, super small.

21:58Yishai Beeri:It's that they've stacked a bunch of those windows on top of each other. And managing that context and how you set up all of that work, get it going during the day, check back in on different things and then close it all out, that's a whole new style of working that engineers are still getting comfortable with. And that translates itself into things like metrics and understanding like our PR merge rate and how big are these PRs. Like it shows up in a lot of different places. I also really love that you called out that in this case of, yeah, you could totally game this metric, but in gaming it, you're actually probably doing everyone a favor because you're writing smaller, cleaner, more atomic PRs and you're just working in probably a more, better fashion for your teammates to review.

22:42Yishai Beeri:Another thing, too, that I think is just so, so critical, and I got to just touch on it again, is the idea of avoiding the rework problem. Being really aligned up front now is the biggest task at hand. Talking with your peers, talking with your stakeholders, talking with your customers, getting really aligned about what it is you need to fix first before you start throwing tokens and people at it. This is what really allows folks to stack these windows on top of each other. They can trust that they did their homework, that folks know what they want, that the agent has the tools they need, and they can come in at the end and review and provide the quality gate.

23:18Yishai Beeri:And this is what allows folks to get a lot of that. That's what really I think is accelerating the widening of that gap. I know myself as someone who runs a lot of sessions and works with agents all day when I talk with folks who don't have the parallelizing action. That gap really is evident in conversations even. And so going back to what's driving all of this, you know, and that is the token spend. I'm really excited that we refresh these benchmarks because we get to reflect all of these new changes and everyone's working a pattern since the top of the year, which, as you called out, are pretty stark compared to the end of last.

23:54Yishai Beeri:And now we're starting to get really early data on how folks are putting a number on their token spend. And, you know, customers that we're talking with, they're watching that bill climb in a way that they can't really necessarily feel like they have a grapple on. And it gets really hard to tie that to results. So, you know, I'm kind of curious, like, obviously, the anxiety around that conversation is kind of everywhere. When you go and you talk with folks about how are your teams using AI? How much is it costing? What are you getting out of it? What stands out for you for companies where you talk with them?

24:25Yishai Beeri:And, you know, maybe the rising AI budget, it stops being a worry and it becomes the whole point. Like they're fully leveraged on it. What do you think really stands them apart in conversations? One common thread that I'm hearing is, yeah, budget is or cost is a concern. We have to get better at measuring it.