How monday.com paused its roadmap for 30 days to hit AI escape velocity | Sergei Liakhovetsky

**Andrew** (0:05)
Today, I'm thrilled to welcome our guest, Sergei Liakhovetsky, the VP of R&D at monday.com. Sergei, welcome to the show.

**Sergei Liakhovetsky** (0:14)
Nice to be here, thank you for hosting me.

**Andrew** (0:17)
Of course, we're really excited to have you here. And today, we're talking about how Sergei didn't just roll out AI to his team. Instead, he paused the rest of the roadmap for 30 days and pointed a 700-person technologist organization at AI enablement as the goal. And they came out the other side with something pretty rare. Every developer using AI daily, new platform capabilities are already seeing adoption numbers that most teams are not hitting. And the numbers, they do speak for themselves. We're talking about Monday Magic with 5,000 solutions built in under 3 months. And Monday Vibe with 40,000 apps created in 2 months. Sidekick 150,000 interactions in less than a quarter. And under the hood, you're talking about an insane acceleration of one of their biggest tech debt problems, cutting a 33-year investment down to just 5 months of work. And handling time for complex customer tissues dropped from 3 days to 1 Test coverage doubled. Onboarding speed sped up to 21%. We're talking so many gains. So how do you build for humans and machines at the same time in this world? And what happens when your organization hits AI escape velocity? That's what we're going to find out today on Dev Interrupted.
So, Sergei, I want to start by talking about the numbers we just ran through. I talked about all the thousands of apps created by your users, the amount of tech debt that you've eliminated, and every developer using AI daily. And none of that happens without a lot of trust and reliability underneath. And when we talked initially before this call, you said something really stuck with me about trust being the currency for all of this. And this is the foundation that lets you ship all of these amazing features for your users. So when you look at Monday.com's journey from B to C to Enterprise Scale, what were the first cracks that showed, that told you, oh, we need to rebuild this foundation to get ready for this new era?

**Sergei Liakhovetsky** (2:15)
Yeah, so it's a great creation, Andrew. And I think that we need to start first of all from the culture of Monday. Monday is a great company where we're focusing a lot on the customer experience. And when we're focusing on customer experience, we are looking at how actually our users will use the system. And this was the main driver for whatever we did so far. So first of all, the UX, first of all, the experience, and after it, we'll look on how the system should work. So at some point, when we continued working up market, actually we saw that the system is lagging behind from performance perspective and from scale perspective. And this is where we started looking on the different solutions, what we can do in a different way. And this is how the MondayDB, the first version of MondayDB, right now we're already in the third version of MondayDB, raised. We started looking on how we're building absolutely different way, how we're dealing with data. So, from one side, we had the trade-off, okay? Because when you want to have a great performance, you need to think how the user experience will look like. And this is where we decided that we initiated this project of MondayDB. And this project ran for two and a half years till we had the first release. Actually, we replaced the entire underlying technology with the data management system that we built. We are using different foundations. We started from SQL, moved to Cassandra. Now we're using also in cache databases like DuckDB and others. And absolutely different. So we started when the system was supposed to deal with the boards, boards, you know, when mandates like tables. Right. Okay, that is keeping a thousand of items. And now we can maintain millions of items in one board, in one entity. And this is huge. So first of all, this was the first driver of moving forward with the foundation to support our customers, to gain their trust, especially when we're talking about enterprises, about large customers that are looking for predictive solutions. They are looking for the solution that will work with greater reliability, with the greater availability. And this is what we did the first. And the second one that we're doing right now is the cell architecture. In the cell architecture, we are focusing on reducing the blast radius. How we're going to reduce the blast radius of incidence, so in a way that one noisy count will not take the entire system down.