How LinearB helps Kraken find hidden bottlenecks across thousands of engineers | Nik Sudan

**SPEAKER_1** (0:04)
Today's guest is Nik Sudan, Engineering Operations Lead at Kraken. And in this session of LinearB's AI Enablement Interview Series, Nik's going to share his insights and structural lessons about scaling AI maturity across engineering teams, including their hands-on experience deploying tools like the LinearB MCP Server. So, Nik, thanks so much for joining us today.

**Nik Sudan** (0:26)
No, thanks for having me. It's been really awesome to be here.

**SPEAKER_1** (0:30)
Awesome. Well, I want to go ahead and dive in about something that's really top of mind for a lot of folks that are working with AI tools is they start with this phase of just like adoption and then experimentation, and then ultimately there's a mandate or a need to operationalize it and reach production. It can be really hard to ship AI-powered ideas from the pilot to production in order to have like long-term value. So like in your experiences at Kraken, what were the things that broke down during like transitioning from pilots to production and how did y'all overcome being able to operationalize with AI?

**Nik Sudan** (1:04)
Yeah, great question. So I love shipping stuff really quickly and in this day and age, I love proof of concepts as well. And everyone at Kraken loves it. Even before this whole AI renaissance or slopper get in, however you want to phrase it, right? But we have always been moving really fast, right? We always value that kind of startup vibe, try and keep teams lean, you know, stuff like that. And every day we look for ways to execute faster. And AI has made it extremely easy for everybody, right? Including non-engineers, which is where this can definitely have a greater effect than how it was before, right? We don't want to block anyone from building out kind of pilot's proof of concepts, right? So transitioning from pilot proof of concept to production has always been quite tricky. And I think today, it's even easier now. And it breaks down in a few predictable ways. So I want to call out some of the problems, because I think knowing the problems will help mitigate it. But then I'll also give some examples of what we're trying to do to avoid it as well. So, number one, spending too long on your proof of concept, on your pilot, right? It should be a quick and dirty couple of projects, that quick and dirty thing, take you a few days or a week maximum, right? Get something out the door. That's why it's called pilot, a proof of concept. You know, you're trying to get the point across.
So don't overthink architecture or design. You know, engineers will spend a lot of time on architecture. You know, designers, more official people do design. Don't, don't, just, just for what is the kind of core essence of what you're trying to get across? You know, like, you know, when showing the BlackBerry proof of concept, you know, a dumb phone just to show investors like, that's what you're trying to do, right? And just because we can build stuff in software doesn't mean that we should, you know, build something that's five times as good. Still just keep it simple. Spending weeks polishing a proof of concept and you, I think you've lost the plot.
So secondly, treat your proof of concept as the foundation for your real thing. Don't do that. You know, don't treat it as the foundation. If you're building a scalable product, you will need proper architecture. Proof of concept doesn't need any of it, right? Just goals are completely different.
Promote proof of concept straight to production, and you're probably going to get a lot of pain later on, right? So don't be afraid to work on throw away code projects.

**SPEAKER_1** (3:30)
Yeah, totally. It's about like showcasing what could be the end result more than about trying to lay every brick of the path to get there.

**Nik Sudan** (3:37)
Yeah. And again, I think a lot of engineers try to think about this and stuff. And I guess the mistake is promoting that to your production product, right? And this is where, I guess the third thing I would like to bring up, which I say is, I would say is more of a newer trap. So, you know, now that everybody has AI, everyone could build something fast. You know, you go on X, you know, someone, someone's shitting their startup idea, and then a week later, they tweet, oh, sorry, they post, oh, this broke, my secrets leaked to production. I guess I won't be committing them. It's like, yeah, for us, it's like no shit, of course, but, but, you know, with AI and building something fast as well, there's a temptation to think it, you know, the speed, just the high output scales consistently all the way to production, right? So you can iterate quickly, get things in front of people quickly, but making something that's scalable, production ready system, you're still going to need to take time with it. AI can help and will help you make it faster, but you take out all of the thinking, you get agents building it, it's gonna, it's gonna break, you know, pressing one button, telling, you know, turning on auto mode on Claude, isn't going to ship you a money maker, you know, it might ship you something that looks like it does, but it doesn't, right?