# Monday.com's AI Strategy: Infrastructure First

**Podcast:** Dev Interrupted
**Published:** 2026-03-03

## Transcript

Today, I'm thrilled to welcome our guest, Sergey Lykoveski, the VP of RD at Monday.com.
Sergey, welcome to the show.
Nice to be here.
Thank you for hosting me.
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, 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 three months, and Monday Vibe with 40,000 apps created in two 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 five months of work and handling time for complex customer tissues drop from three days to one, 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 depth 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 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 B2C 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?
Yeah, so it's a great question, Andrew.
And I think that we need to start first of all from the uh 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 the how actually our users will use the system.
And this is what's the main driver of what whatever we did so far.
So, first of all, the UX, first of all, the experience, and after it will 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 Monday DB, the first version of MondayDB.
Right now, right now we're already in a third version of Monday DB raised.
We started looking on how we're building absolutely different ways, how we're dealing with data.
So from one side, we have the trade-off, okay?
Because when you want to have a create performance, you need to think how the user experience will look like.
And this is where we decided that we initiate this project of MandyDB, 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 DougDB and others, and absolutely different.
So we started when the system was supposed to deal with the boards, boards, you know, and 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 creative ability.
And this is what we did the first.
And the second one that we are doing right now is the cell architecture.
In a cell architecture, we are focusing on reducing the blast radius.
How we're going to reduce the blast radius of incidents so, in a way that one noisy account will not take the entire system down.
All that together gives a lot of trust to our current to our customers.
And this is what we're pushing ahead.
Because for us, customer experience is trustworthy customer experience.
So it starts at the beginning by understanding that you had to fundamentally improve your underlying infrastructure and architecture to handle the scale and the complexity needed by those customers.
And that's nothing glamorous.
That's like getting in the plumbing and fixing things and ripping things out and making it better performance-wise with databases.
This is before any kind of glamorous AI work.
Yes, right.
Yes, absolutely.
So if you're looking on the Monday journey, I think it's a bit different journey from uh other companies, other startups.
First of all, Mandy started from user experience, and after it moved to deal with performance and scale for larger customers, and after it we moved to AI.
So we're always looking for how to improve the experience for our customers.
How do you build this culture where investing in those foundations to make that great customer experience is seen as like a first-class thing to go after?
And it's not just slowing you down.
How do you align the culture around that?
Yeah, look, when we're looking on foundations, first of all, we're looking at like for me, foundation is equal to standards.
So if we are going to our bigger customers, we want to gain the trust and we talk the trust is the currency for our customers and trustworthy experience.
We need to make sure that whatever we're doing, whatever we are building, we're building like a highways.
Okay.
And highways to build the highways, it takes time.
But in many cases, when you're building this highway, have unlimited speed later on.
And this is exactly our approach here.
When we're going to provide the experience and where we need scale, where we need performance, we are building those highways.
We're building the robust foundation.
We're using cell architecture, we're building our foundations, our resilient data layers, our additional shielding tools that are shielding and preventing incidents and providing resilient behavior.
But again, it's not always the case.
For example, right now, when we're talking about AI, it's not enough.
We need to know how to balance between robust foundations and moving fast.
So we are working as the builders all together to identify where we need to move faster, where we need less resilience and less scale, but we need to have speed.
And we are building those products.
So experimenting from one side, and on the other hand, to have the robust foundation for the solutions that should run in scale and should run with the high performance.
Yeah, there's a really sharp observation, and something we're going to talk about more a little bit later, too.
The idea that you know you have to get the foundations together in order to go fast.
And the highway is a promise for speed in the future.
That's just a cool thing about a highway.
You can't build a highway in secret.
Everyone knows the highway is coming and you're going to go fast in the future.
So in the same way, you get everyone aligned around the same goal.
We're going to make our foundation amazing so it sings that way we can go really fast.
Because we are in this like difficult environment.
Engineering leaders every day are having to make tough decisions between do I double down on our infrastructure and spend more time fixing our technical debt?
Or do we just innovate in speed at the speed of light and just hope that we just catch something that yanks us forward, you know?
So it's like a real, it's a real balance, I think.
And in this world, there's lots of metrics you can use to navigate the success of fixing your underlying foundations and uptime and data integrity, those are just, you know, those are pretty straightforward.
But what other signals do you or Monday.com look for to understand how trust is increasing or eroding with your customers?
Oh, it's a it's a lot of different metrics.
It's not only about uptime, it's uh also about performance.
I think that in the new world, especially within AI, when we're looking for, or customers basically are looking for a different uh latency, and they are looking for instant responses from the system.
Performance is like if we have the high latency, it's like a download.
We cannot uh sorry, it's uh like an outage, we cannot afford it.
Right from one side.
So performance is one of them, definitely.
And we are defining the core flows, and we're looking on each and every core flow to see upload, download metrics, and also the single interaction metrics.
So we're standardizing all of that.
We're looking exactly every day, every minute, what's going on in the system.
On the other side, it's also soft metrics like uh the sentiment of the customers.
So we're running the service with our customers to get their sentiment.
And when we see the sentiment is going up or down, we're analyzing analyzing it, we're trying to figure out what's going on, and we're moving to the metrics in the system to correlate between the sentiment that we are getting to what we see in the system.
That's very cool.
So there's both trailing and leading indicators that you're looking at around this experience.
Exactly.
And it's always balance between both of them to see how we have lead-in, uh leading uh metrics like uh the sentiment of the customers and trailing metrics to understand what is uh happening in the system.
So we've talked a bit about how you create this environment of trust and how you make the customer experience first class from the very beginning of Monday.com to still now it's it's scale and size as enterprise-facing business.
And now that we've kind of talked about maybe some of the less glamorous work of working on the foundation, there is a pivotal time within Monday.com that you explained to me that was really fascinating that I want to talk about before we move into um the different kinds of AI offerings that y'all have built out of that um experience.
And this is your 30-day AI month.
And it can be pretty hard to convince an entire organization to pause a roadmap for 30 days, especially at the size of Monday.
But you pointed all of your technologists at one goal, you know, becoming an AI-enabled org.
And in this month, um, you experimented with a lot of things with your team that I would really be curious to know more about.
Like one of them being like, how do you design a month like that?
So people don't just build demo after demo after demo, but they're walking away with you know durable skills that are gonna fundamentally change how they work.
So thank you for a question because it's uh really interesting, and I think it will be great uh exercise also for other companies and other leaders in other companies.
Because you know, before we uh moved or even thought about AI months, it was like we worked with AI, we looked at it like a features, but it was like a table stakes.
You have features, some teams are developing it, but it's not something that is transformational for anyone, not internally and not externally.
And with the leadership, uh myself, Liron, who's another VPRD, and also products started thinking about it what we can do differently, how we can engage the entire builder's company.
It's like a company.
We have about 700 people in builders.
So 700 engineers, we need to engage, we need to inspire, and we need to give them some tools for experimenting, hands-on experimenting.
Otherwise, if it's only education and training, uh it's uh it's nothing for uh such a huge company like uh like we have in Monday here.
So we started thinking about it, and uh we defined to ourselves several principles.
So the first principle was we are not going to run it like a hackathon.
Because when we're talking about hackathon, an hackathon is very important, vehicle mechanism to to reach the inspiration to innovate.
But in our case, we wanted to achieve something else.
We wanted to make sure that whatever we are doing is reaching production, that we understand not only what to do, but also how to do that.
So the first principle was whatever we're doing is going to production.
We are not going to do something only for the sake of experimenting here.
From the point we decided that we wanted to have AI months to the point we started, we kicked it off, it was only two weeks.
Some mistakes.
If some mistake will happen afterward, we'll change and we'll fine-tune as we are going.
So the second point was to go through all the teams, and we have dozens of teams in Monday.
And to make sure that we know how to deal with each and every commitment that we have to our customers.
And at some point we figured out that most of the work that we are going to do, we can do also with AI.
It's not like we need to stop everything and uh to start uh doing some isotheric things only for the sake of learning here.
So this was the next principle that we defined to our teams and to ourselves that whatever we are doing, we need to make sure that it uh accelerated in our broadmap.
Even if we need to reprioritize, but at some point it should be fitting the product that we are doing.
And again, at some points we saw that it doesn't fit, and that's fine.
But those were like exceptions here.
This is how we started working.
This is how we kicked it, this is how we designed it, and after it, it was a huge amount of work to work with the teams and to run demos, like you said, and to have education, and we had the champions program in parallel to that to make sure that people have communication channels and know how to get the data and to get the knowledge they're looking for.
It was from one side, it was a a huge amount of work.
On the other side, it was so inspiring that people started uh actually fighting for going and uh showing the presentations and showing the demos of whatever they're building, and this was great.
This is how we built the new products, like uh you mentioned before, Monday Magic.
This is where they started Monday Vibe, and also sidekick the copilot that we're using as system, and also internal projects like uh Morphics for splitting the mono lead and sharelook, where we succeeded to reduce the amount of uh time we're spending on tickets resolution by half more or less.
So if you give to people the ability to fly, they are flying just to give them ownership and give them to run.
I think this is the main takeaway from uh from VCI months.
I think that's a great lesson to learn from it, and I think that's uh really hits at the heart of why developers are developers.
Like we're software engineers because we want to build cool stuff because every day we want to go to work and build something interesting that changes lives that you know is is is intriguing, but also just makes the world better.
Like we're curious and we want to tinker.
So the idea of having the AI AI months and aligning it around some ground rules.
I want to I want to run through them because they're really smart.
Um, one of them being that anything that you put together, it's not just throw away hackathon.
Everything has to be aligned towards a business purpose, a goal.
What was going to be worked on should have the intention of being taken to production, but then taking it one step further and working with all of the teams on an individual basis and understanding their needs and commitments to customers, and then you actually get an ability to map that into your experimentations with AI and what you're going to build and roll with.
So now you're not just throwing like AI at the org and saying, figure it out.
Let's let's let's learn how to roll with this.
Let's see what we can make.
Instead, um, you're creating like a an art of the possible.
You're you're showing a space where people can come together and ship and show best versions of what they think the product could be.
And I think that's the most exciting version of like AI months that can happen inside of any company, is you get somebody who can take the the core idea, the market position of what you provide to your customers and take it to that next level with AI as like a concept.
It helps align everybody, right?
Around the idea.
Because right now we don't know what we're looking for yet.
So I I like how you um kind of used the carrot on the stick, so to speak, like the guide, the experimentation towards what um the customers and stuff would ultimately benefit from.
Yeah, I think that you know, people need some space to experiment they they when they are looking for the mind shift, the mental mindship.
And what we're uh going through with the eye right now requires absolute mind shift in the way you're working, in the way you're looking at the stuff, in the way you are practicing, and in the way you experiment it.
If you will not be able to experiment, if you will not give teams to experiment, it will be a failure.
It will be like uh like everything.
And what once you provide this ability, those tools to people to fly, they are doing miracles.
It was amazing to see all the speakers, all the demos.
You know, overall we had allowed around uh 17 workshops only during this uh AMI months.
17 workshops.
We had about 22 speakers, and we had uh yeah, and we had about 70 demos or something like that.
So just a huge number of participation, everyone's really excited about figuring out how what we can do with this and where we can take it.
Yes, and people really loved it.
In this world where they're like using all these different tools, you know, are you just kind of letting them experiment and grab whatever they want?
You can pick use this tool, you can use that.
Like, how do you start to keep tools in check and understand like what is our tool library going to be emerging from this month?
Yeah, so uh first of all, I think and uh again it's a great question here because uh we started working with one tool, uh actually with Scarcer, and we figured out that limiting people is limiting their imagination and their ability to move fast and to experiment.
So we define absolutely different uh methodology and different processes here in the way we are working with tools, in the way we are acquiring tools, in the way we're experimenting.
So we're absolutely remove the barriers.
Actually, personally, myself worked with security procurement and legal to define exactly how we're working from the one side to make sure that from security perspective, we are not uh putting our customers in threat.
So we worked with demi data, for example.
On the other side, we had zero bureaucracy policy here and zero bureaucracy policy actually to make sure that in the one week you are getting all the tools you want to get, but you need to be champion of the tool if you are asking for this tool.
You need to make sure that there is no PI threat there.
And if after two months the tool is not used, we're just removing it for our catalog.
So you just basically kind of just like open the door, let anyone for anyone bring the tool forward that they need to get their best job done.
And then and the trade away is that I love I love how you I love what how you smiled when you said, you know, it's a no bureaucracy organization.
Give the o you you own that tool, which is I think is easier.
Uh it's pretty pretty easy to do if it's a tool you're really passionate about.
You want to use like, oh, I don't want to be stuck using cursor, I want to use this other tool.
It's like it's you're more likely to get like a good champion who can help others than to learn how to use and get the most out of the tool, right?
Exactly, exactly.
And the made amazing effect, amazing effect, because people feel that they are owners, and they wanted to make sure that the others are using those tools.
So it was a lot of communication around it.
It was a lot of talks.
It was a lot of smiles there.
No, I love that.
Cause then it's like, oh, this is my tool.
I want to use this tool.
So if I want to keep this tool, I should convince everybody of the thing.
Yeah, look how it's amazing.
Yeah, exactly.
Look at my demo.
Look at what I built.
You know, I shipped this with this tool.
This is the future.
So uh it I what I love about this month you're describing is it's the right blend of incentives.
You have the experimentation, you have the the career growth, you have the alignment towards where the company's gonna go next.
You're tapping into all of that excitement.
But what I love that you just mentioned too is that you also worked with security to keep things safe, that make sure there are boundaries.
You know, what what did that look like just from like a bird's eye view of just making sure um you had the right fences up for that month?
So first of all, we worked with the app security team.
Okay.
So up the application security team was part of the committee where we decided what we can do, what we cannot do, how to shape and design the MCP, for example, when we're working with the tools.
They were really engaged.
Like, you know, uh they're not the every app security team is really engaged.
And in this case, it was amazing.
Amazing effect, they worked side by side with developers, like real builders.
So, for someone that's listening to this and they want to replicate an AI month within their own org, maybe they're in a similar like leadership position to yourself.
What would you absolutely repeat and what would you change if you did it again?
So, what I'll repeat is definitely uh look for results.
Okay, it's not like uh like I've said uh training or education for the sake of education.
Whatever we define, like uh we're doing in these months, we need to define also what kind of results we want to achieve.
The second one I think it's uh decision to action.
It took about two weeks for us, and it sounds really quick, I would say, from the make from the decision making to the kickoff of demands.
Today, if I'll do it, I will do it in one uh in one week because the excitement should be like a boost, okay.
It's not like uh something that you need to work on it and to have building uh all the processes around it.
If you will start building the processes when you want to boost the mental mind shift, it will fail.
So whoever is going to do like we did the IMANs should uh make sure that from the point they decide that they all in on for it, should be immediate.
The bottom-up ownership, I think they're one of the most important things here.
Like I said, if you uh give people ability to fly with the tools, with the decisions, with uh the methods, how they're working and what they want to achieve, they will do magic, they will do miracles.
So in this case, our work was really easy as leaders, leadership team, just to provide people the zero bureaucracy, like we said, to remove the barriers and to give them to run forward fast.
I think that one of the points that uh we need to make better next time is uh first of all uh the continuity.
Whatever we start, we need to make sure that either we know how we finish it during this month or how we're dealing with that after the months is finishing.
So we had several golden initiatives where we continued working after the months, and uh that's fine.
Uh, but it moved our priorities, it moved our scope, and uh it took some time.
So once you decide what you are going to do, you need to know how you're going also to finish it.
And again, it uh that that's fine to do mistakes, especially when you're doing something first time.
I think that next time we'll think about the scope uh a bit better there, okay.
So, yeah, this is more or less the points that I wanted to mention here.
No, it's really useful.
I think a lot of folks would be able to take these into the their own future experimentations.
I love the idea of like give constraints, uh, but then also align it towards like we're gonna have closure, right?
We're gonna either ship this or we're gonna know what happened with it and we're gonna close the book, but don't just experiment without the constraints.
Don't experiment without coming back to see what those experiments did.
I think that's something that we talk about a lot here on Dev Interrupted, um, about like measuring and understanding the adoption and also the impact um of those tools and initiatives.
And so I want to talk um just in our last segment here about like the result of that AI month.
And it created this whole ecosystem of products, and now I understand why.
You've taken us on this journey, everything was constrained around this needs to align towards a customer usage.
This is gonna be something we take to uh this is something we're gonna ship as a product.
So now it makes sense that there's four offerings, and you have magic and vibe and sidekick and agent factory, and maybe on the surface, maybe it sounds like I know what they do, but assume I don't, you know.
Well what makes these four uh tools like fundamentally different from each other.
So uh actually are different tools and for different uh purposes and intents of the user.
So when we're talking about Monday magic, Monday magic is how to build solutions when you build the first solution with Monday.
So it's a for someone who is uh the builder starts to build, for example, uh some solution like a library in a university, or uh to uh create the shifts in a hospital or anything else, okay, or solutions with CRM, for example.
So this is about Monday Magic that you're working with the prompt and you're getting reference implementation here.
The second solution, Monday Vibe is where basically you can uh build the applications uh that uh relying on uh Monday entities, on Monday boards, on Monday dashboards.
And uh I think this is huge because whatever you're doing, you don't really need to know Monday.
Just work in the Monday environment, you're building the applications, and you can continue working with those applications later on, and we see a lot of traction around it.
Uh the third one is a sidekick.
Once you already built solution and build applications, you have a copilot, it's a horizontal copilot.
So use a user or the user or the builder of Monday, you can use sidekick that will continue helping you to do whatever you want to do in the system, to fill the boards with the items, to remove the items from the boards, to connect between different boards.
You can ask through prompt sidekick to do that, and sidekick will do that for you.
And uh the last one is the agent factory.
Maybe they don't know the last one, maybe I missed several.
We talked about the columns and blocks.
We're doing a lot of work.
But when we're talking about agents factory, so basically agent factory allows you to build the vertical solutions.
And when you're doing uh building vertical agents, those vertical agents can work with sidekick.
So you will have exactly the same context.
You will share the context of Monday between Monday count between different AI solutions and the AI tools.
And when you're sharing the context, actually you have like a compound effect here that is bringing a lot of value to our customers.
So I think this is exactly what we are looking for, and we'll continue in reaching our portfolio of AI with more products and more solutions.
So all together will provide us the absolute compound effect and help to our customers.
It's really fascinating to listen to you describe it because it's like these different levels of AI and your comfort and technical level familiarity.
And you know, most companies are trying to shipping one thing, but you saw in your user base that a one-size-fits-all solution wasn't going to work.
And from how you ran through it, like magic is it's like prompts.
It's it's it's simple prompts to get simpler things done, and then you can go a level higher, right?
With vibe, you can kind of orchestrate these apps on top of the platform on top of the tool.
And then you can kind of work with sidekick.
Now you have an assistant, right?
It can uh understand everything within your Monday.com world um and probably trigger a lot of these other workflows from it as well.
And then you have agent factory, which is like a level of abstraction above that.
So no matter how technical you are uh or aren't, you can come in and pick up one of these tools and make it fit for you.
And it's going to make Monday fit like a glove, fit like a glove for whatever you need it to do or however you log into Monday.com, right?
Um which is really really fascinating.
Um is that kind of like what you saw in your user base that led to creating that suite?
Yes.
And the uh this is exactly the point.
Uh because you know, we are learning our users, and the Monday platform is very uh widely used in different uh industries and for different markets.
So once we uh providing vertical agents on one side, the solution on another side, and horizontal side key copilot on the uh the short vector, basically we provide our users with the ability to decide how to work on one side.
On the other side, we are learning about the user to improve the context and improve uh the experience.
Because we understand the intent of the user.
And the knowing the intent of the user and knowing the data about the user create brings a lot of value for the user itself because we can uh provide more better quality for for uh the agents that we're building for them.
No, I I love that bit you called out about how it helps you understand what users come to you for, why are they using Monday?
It's like just as much as they get more value out of your platform now, you also now get to more intimately understand why they use you and why you're sticky for them and how you can meet them where they're at and provide things that they don't even know that they need yet.
And uh I think that's really fascinating.
I love how you mentioned how they kind of compound on each other.
That's something that stands out to me is if you have these different ways between them, you can triangulate some level of technical familiarity and domain level expertise to execute something with these tools.
So, how do you create the boundaries when you're engineering in that space?
I imagine in the beginning maybe it was a little fuzzy, but as they've come to the come to be like full-featured products, what's this mental model within Monday.com that separates like what is a sidekick problem from this is a vibe problem.
Vibe uh problem is uh to give use user ability to build the application that you are looking for.
Okay, you're already user in the system, you're building the applications, you are using those applications.
You can publish those applications for other users to use in the account.
Okay.
When we're talking about sidekick, it's a different uh different dimension.
You already working with your entities, you are working with your dashboards, with boards.
You are already got the information that you want when you built this application.
And now with Sidekick, it's like a horizontal copilot in uh other places in other applications.
You can do whatever you want in the platform level, okay?
But in the platform level, you need more intent.
You need to understand more context.
And you are getting more context from uh the vertical solutions, like uh CRM agent, for example, or work management agent.
It's a vertical solution that understand the customers.
And this is how you define the boundaries.
So sidekick more horizontal one.
Uh vertical agents were building with agent factory, okay?
And the applications we are building with Vibe that can work in a scope of board or in the scope of applications or in the scope of account.
So, what what does this do to your like infrastructure that we talked about at the beginning, right?
You did all of this hard work of making sure the foundation underneath this was sturdy and steady.
With all of these um agents and all of these AI features on top of of your code base.
Um, like did you encounter more constraints or problems with like your API?
Like suddenly, oh, we're getting way more requests, and maybe it was fine because you spent all that time at the beginning preparing for it.
But like, what did what did you see change internally in your infrastructure once you started to roll these things out?
Yeah, so definitely agents are uh interacting with uh with the system, absolutely different from uh human beings, and we already see it.
We'll already see that we have fan out of agents.
We have a lot of API calls that uh we're looking and see how we're going to have a fairness index, for example, to make sure that the one account is not taking all the resources.
We're looking also on concurrency to make sure that the agents get the fair resources.
And again, we're moving when we're working with uh with AI, we're moving from SaaS that is CPU bound to SaaS that is GPU bound.
It's absolutely different.
It's like you know, like uh from one side uh getting resources like uh expensive like uh Ferrari, okay.
On the other side, uh you basically need to know how to manage them.
So we're building the uh guardrails, we're building the schedulers to make sure that we know how to manage the costs here.
So it's not only about how to provide the resources, but also about how to manage the cost of those resources.
And uh I think it's uh absolutely different way to to look on the metrics and to look on the SLOs.
It's a different type of SLOs here when you're looking on the cost on fairness index, when you're looking on the concurrency and the agents utilization and how to deal with fan out here.
So was this like it opened up a door to like a like a like a new world of reliability that you had to offer?
Because now suddenly your engineers are building differently on your platform.
Your users are using your platform differently.
And now there's this new cohort of users, these AI agents and bots that are also slamming your APIs and systems, right?
So really it sounds like from all of that, you couldn't just innovate and go really, really fast with all these new products, but you also had to, you know, going back to the very beginning, make sure you had the right foundations that to take them at scale for how Monday.com customers expect.
Right foundation and also right security guardrails.
We didn't talk about it before, but uh in a new world of AI, we need to make sure that our customers are protected, that we have a right segmentation of the network, that we have a right guide rails around the counter isolation here.
So it's uh absolutely different story here.
Whatever we had so far is changing right now.
And we are changing together with that, and the infrastructure is changing.
Working with GPUs and working with the new way of work and new way of interacting with the system, like agents are doing.
We need to build different APIs.
We need to optimize those APIs for MCP use and for agents use.
It's a different story at all.
Yeah, you're just like talking about this new frontier of all these things that you're gonna build.
And honestly, in talking with you, Sergey, I get the vibe that like we're you're gonna we're gonna end this call and you're gonna go back to building it.
It's like you're such like a builder's person, and I can tell you're so passionate about bringing this stuff to production.
And is it really amazing to hear this story?
You know, you've taken us inside of Monday's foundations and their AI ecosystem journey, how you'll evolved your 700 technologist organization to become AI ready, but also aligned around goals and making products that your customers would actually use.
And I think that I learned a ton of stuff about how I would model this internally.
I know our listeners did as well.
But before we wrap up, where can our audience go to learn more about you and the tools from Monday?
So, first of all, from uh Monday.com, of course.
Uh, we have a great blog, we have great articles, and uh everyone is welcome to visit our site to learn about our tools.
We have a lot of uh articles and blogs that we're publishing also on LinkedIn.
I guess it posts.
And uh, I will be happy to answer any question of all the users of the this episode.
Awesome.
Well, we'll include those notes in um the show notes.
That way people can go check out those links.
And thank you to everybody listening today.
But the conversation, it doesn't end here.
Like Sergei said, we want to continue it on LinkedIn.
Please come find us if you have questions, uh, curiosities, concerns, anything about what we talked about today.
We would love to know about how you are using AI within your org, but also what you're taking away from this conversation.
And so join us on LinkedIn.
You can also find us on Substack, just look for the Dev Interrupted newsletter.
And that's it for this week.
See you next time.
And Sergei, thanks again for coming on the show.
Thank you for having me here.
Thank you.
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