Nobody is shipping your agent’s code (yet) | Predictions from LinearB’s Ori Keren

**Ben Lloyd Pearson** (0:05)
Hey, everyone, it's your host, Ben Lloyd Pearson. I'm here today with Andrew Zigler, and we're joined by Ori Keren, co-founder and CEO at LinearB. Ori, it's always wonderful to have you on our show.

**Ori Keren** (0:17)
It's great to be here, and it's fun for me, too.

**Ben Lloyd Pearson** (0:19)
Yeah, we love getting your predictions every year, so it's an annual tradition, I feel like, at this point to have you come on our show and just share what you think the next year looks like for engineering leaders. And of course, we sat down with you last year, and I actually have to give you credit for your prediction. You know, it was a little bit contrarian, maybe a little controversial, but I think it turned out to be spot on. And before we get into your predictions for 2026, you know, I want to look back on last year and when everyone was hyping up how, like, AI was going to 10x engineering and your engineering output, and you went on record and said that productivity would actually go down in 2025 And like I said, I think that was a great prediction, and I want to ask you about that. But first, I wanted to see if you had any bold predictions for this next year.

**Ori Keren** (1:05)
Yeah, I hope it is as bold as the old one, as the previous one. But I think my prediction is that it's still going to be very interesting in code generation. New stars will pop up and new hype will be there. But we're still not going to see the 2x, 3x, like, productivity improvement that everybody is expecting to. So that's my prediction. Maybe not as bold, but I still believe that this is a year of, like, norming, if you will, like, before we get that promise.

**Ben Lloyd Pearson** (1:41)
Yeah, well, I mean, if you consider we may be in peak hype, that may be actually a pretty bold statement to make right now. So I got a lot of questions that I want to ask about that. But first, I just want to give you a chance to reflect and look back on what we shared a year ago and just see how things have played out in that time.
So one of the big arguments that you made back then is that the friction of adopting new tools and the natural resistance to change would slow teams down before it sped them up. Like I said at the top, you described a dip where teams would have to figure out how to work with this new technology before they actually receive many of the benefits. Looking at the state of the industry now, do you feel vindicated? Do you think we actually went through this productivity dip?

**Ori Keren** (2:26)
Yeah, I actually think we did, or at least we stood still in the same place. You can look at data that is out there. We see that there's 30 percent more pull requests, for example, that are being created. That's great. But as you go downstream at the development pipeline, you see that it's actually maybe 2 percent more that are being released because there's a lot of gates that it's being stopped. We saw, I think, as an industry, a decrease in the stability and the quality. There's research that's talking about it. I think the DORA metrics are speaking about 7.2 or something like that, decreasing the stability and qualitatively, people are talking about it. So I think if you balance all of it, we're actually had this deep, or at least we stood still and we're still learning how to utilize these tools right.

**Ben Lloyd Pearson** (3:22)
Yeah. I really like that you brought up DORA because I think the research report that came out earlier this year really is a big part of the vindication because one of the statements they made was that upstream velocity increases are lost to downstream chaos. So even if you're moving faster, there's still so many other aspects of our SDLC that haven't been impacted in the same way by AI. Yeah.

**Ori Keren** (3:45)
I absolutely agree with that. There's so many factors like, it's almost like, and we can elaborate on that later, but it's almost like going back to SDLC fundamentals, like what are the phases? Where does AI really play? Where does it give us the productivity gauge? And where does it hurt us or take us even back?

**Andrew Zigler** (4:05)
Last year, you also made a bold prediction around our adoption of AI agents, and you were spot on that 2025 would be a year of experimentation versus full adoption. But now that we have spent that full year experimenting, it's 2026th year that we hand over the keys?