Rebuilding IT From the Ground Up for the AI Age: Serval's Jake Stauch

Jake Stauch, founder and CEO of Serval, is building a ServiceNow for the AI era. His most contrarian bet is that the product should look like boring old enterprise software, but with unlimited intelligence. Serval's architecture splits work between two agents: an admin agent that uses code generation to spin up workflows from natural language, and a help desk agent that can only act through the tools admins explicitly approve. Jake explains why his team uses OpenAI models for end-user interaction and Anthropic models for code generation, why new model releases sometimes have to be rolled back when prompt tuning breaks, and why he's not worried the foundation labs will come downmarket. He also makes the case for "fewer, better" hiring as the only durable moat in a world where products may need to be rebuilt every six months. Hosted by Pat Grady, Sequoia Capital

• 0:00Speaker 00

  You know, I think that there's always a gap between the idealized vision of what you think your job's going to be and then what your job actually is. I think it's true for every profession. You idealize, and kids do this the most, right? When they're like, want to be a firefighter, an astronaut, what that job looks like. And then you get in that job and you realize, oh, there's actually a lot in this that I don't like. And we want to be the tool that that actually closes the gap between what you think your job's going to be and what your job actually is.

• 0:43Speaker 01

  All right, I'm here with Jake, the founder and CEO of Serval. Jake, welcome to the show.

• 0:47Speaker 00

  Thank you so much. Great to be here.

• 0:48Speaker 01

  All right, we're going to start with a high level question. You guys are building kind of the next generation ServiceNow, like the AI native ServiceNow, so to speak.

• 0:57Speaker 00

  That is right.

• 0:57Speaker 01

  Why does the world need an AI native ServiceNow?

• 1:01Speaker 00

  Yeah, employees need help at work. That's kind of the idea here is we are a platform for employee support. The technical term is enterprise service management, but what it really means is getting help at work. And the ideal way to get help at work is you ask for something and you get it instantly, automatically. You don't have to wait on somebody to track down a ticket and assign a ticket to somebody. You just get help immediately. That requires you to have some kind of automated support. And that automation is best built with AI. So we think about this from first principles. How do we support employees? You automate all the requests. How do you automate all those requests? AI is really a good tool to bring that automation employees.

• 1:38Speaker 01

  Awesome. Let's say more about that. So I remember back in the day when we first met Fred Luddy, who was the founder of ServiceNow in like 2007, 2008 sort of timeframe. And at that time, people thought that ServiceNow was amazing because it was this big step function change over Peregrine and Remedy. And the key thing that they got right was to think about Enterprise software as an abstraction is just workflows on top of a database, right? So they built kind of this flexible workflow configuration engine on top of a database. And at the time, that was amazing and you know, IT people loved it and they were off to the races. What I'm hearing you say is that that's not enough with AI. There's kind of a new generation of automation that can be done.

• 2:23Speaker 01

  Can you just say more about the thing that ServiceNow built? And the thing that you guys have built and kind of the side by side? Like what is truly different this time around?

• 2:32Speaker 00

  They got it right. We also built workflows on top of databases and that is the right abstraction. Those are the right primitives. The problem with their workflows and databases is that they require a lot of manual effort to build and maintain. So building those workflows often requires dedicated development resources to put those together. And while that sounds fine, you invest those resources, then you get a beautiful automation on the end of it. That can take weeks to months. And in an era where business processes are changing very rapidly, by the time you get that workflow implemented, your business may have changed and moved on and you want a different workflow. And so your automation kind of runs behind where you want it to be. And that's more true now than ever before. Same with the database.

• 3:14Speaker 00

  If you need to manually update those entries and look at your IT assets and make sure they're up to date in those systems. That's going to be very, very painful to have to bring in consultants or internal developers to update. We took this unique approach of let's keep those primitives workflows on top of databases, but allow you to use AI to build the workflows and use AI to update the databases. And the way we do that is this code gen engine where you describe the workflow that you want, all the different steps and permissions and approvals and logic. And we take your natural language description and we turn that into code. And so your workflow appears instantaneously. There's practically zero time to develop those workflows. And the same thing goes for our databases.

• 3:57Speaker 00

  You can describe exactly what data you want to take from which sources. And our system will actually generate the code to fetch that data and keep it up to date without you having to do any kind of manual intervention.

• 4:08Speaker 01

  I remember you said months ago, this is one of the things you said when we were first getting to know each other that really piqued my interest, something along the lines of if you really want to drive enterprise automation. You have to make the process of building the automation just as simple, if not simpler, as the workflow being automated.

• 4:27Speaker 00

  Exactly.

• 4:28Speaker 01

  Is that, do you still believe that? Is that still true?

• 4:30Speaker 00

  I still believe that. And that insight came from just putting yourself in the shoes of someone in IT or another function, and you're presented with a task. Somebody asks you to reset their password, and you've got two options. You can go into Google Workspace, find the user, hit a button that says reset password.

• 4:46Speaker 01

  Yeah.

• 4:47Speaker 00

  Or you can open up a workflow builder, and you can drag the trigger, and then you can drag the response, and then you can, you can build out this custom workflow. And when you're presented with that choice, you're just going to reset the password. You're going to do the manual thing. But if it were actually easier to build the automation, you would build the automation, because it's just, why wouldn't you? And I think that it comes down to that. People in the moment have to make that decision, and you want that decision to be very, very easy, to opt for the automation, not opt for the manual step.

• 5:13Speaker 01

  Now, is there, is there such a thing as being too simple to automate? Like, is there, you know, people talk about the vibe coding and all the slop that it produces. Is there such a thing as slop automation?

• 5:25Speaker 00

  Yeah, it is. It's real. And we've had to build some really interesting things around that, because yeah, when you make it so easy to automate, somebody might build the 20th password reset workflow this week.

• 5:34Speaker 01

  Yeah.

• 5:34Speaker 00

  And it's basically the same as the 19 that came before it. And now you've got all these duplicate workflows, and the AI gets confused on which one to run.

• 5:41Speaker 01

  Yeah. How do you guys manage that?

• 5:43Speaker 00

  We, we built a, a new agent, basically, on top of Serval, that's what we're really excited about, that has full contextual awareness of all the workflows you've ever built before, how they work, what they're going to do. So when you say, hey, I want this workflow that does X, Y, and Z, it says, hey, actually, you've got 19 that already do that. I could modify one of these, but here's what I think you should do. I think you should actually delete 10 of them, separate these remaining nine into these different categories, add these approval steps. And so it actually walks you through the system, and it's kind of your assistant that's an expert in our product, to help you translate the business requirements into the actual product implementation.

• 6:18Speaker 01

  Speaking of the product, so you're a product guy. And one of the things that we've heard about you consistently from every possible source is that you're extremely customer focused and like really good at listening to the customers and figuring out what they need. Do you have a North Star metric that you rely on that just tells you the product is getting better and more useful? Or is it more you collect all the anecdotes, you synthesize them, you kind of have an intuitive sense? Like, is there a number that you can look at?

• 6:43Speaker 00

  I think it's the latter. Like, I try to be embedded with the customers, like, full immersion. I am in every single customer Slack channel.

• 6:49Speaker 01

  Yeah.

• 6:49Speaker 00

  I think most of our customers will get a Slack from me and that channel every single day.

• 6:53Speaker 01

  Wow.

• 6:54Speaker 00

  And that is a huge, maybe-

• 6:56Speaker 01

  And just to set the context, a listener might think like, okay, you probably have four customers. How many customers do you have?

• 7:01Speaker 00

  No, over a hundred customers.

• 7:03Speaker 01

  Okay.

• 7:03Speaker 00

  Uh, and, and a lot of large enterprises. And it is overwhelming at times to be in all those conversations all the time. And sometimes I feel like, oh, man, am I wasting my time. But I feel like I really understand what's going well, what's not going well. And, and I just have my finger on the pulse. And there's just no substitute for that. Especially when a lot of the implementation work has gotten very fast. More and more of the moat for any startup is the customer insight.

• 7:29Speaker 01

  Yes.

• 7:29Speaker 00

  The empathy of like, actually understanding what they want. And if we can have a differentiated advantage around the customer insight, that's going to be much more valuable than having a, you know, a product advantage, which is copyable overnight.

• 7:42Speaker 01

  Let's talk more about that because, um, this has been a hot topic for a few years now. You know, ChatGPT comes out in November of 2022, and immediately people start deriding application layer companies as wrappers on top of a foundation model, right? And what you just said kind of plays directly into this theme of there's always a school of thought that says, well, the foundation model is going to just do everything. And then there's a school of thought that says, sure, but you can build a company on top to close the gap between raw capabilities and like actual customer problems. How do you view your role running an application layer company versus the role of the foundation models? Like, where do you think the moats form around your business over time?

• 8:26Speaker 01

  Basically, to the extent somebody listening is interested in, like, how do you build an application layer company in this era? What are your thoughts on that?

• 8:33Speaker 00

  I think you have to be happy when the new models come out. And that is, that is kind of the guiding principle that we have is, how do we make sure whatever we're building is actually not made obsolete by whatever the labs and hyperscalers come out with, but actually is made better?

• 8:48Speaker 01

  What's a good example?

• 8:49Speaker 00

  For us, we think the product is the boundaries. The product is the controls. The product is actually what limits the capabilities of the model. Because the question now is not, can OPUS, can GPT 5.5 do these amazing things? Can they do the things I want to do in my enterprise environment? The capabilities are practically unlimited. It's, the limitation now is, how do I get comfortable as a large enterprise that cares about security and deploying this company-wide without elevating my security risk? And so, we think about it from the boundaries. And so, that means really boring, old-school enterprise software things around permissions and approvals and limiting the scope of your API integrations and having visibility into

• 9:34Speaker 00

  that, having audits and reporting and logs and alerts and just all of the things that make you feel comfortable letting the models run, run wild in your environment.

• 9:46Speaker 01

  Yeah.

• 9:46Speaker 00

  And so, one of the fundamental things we did from an architecture perspective is kind of divide the agents into two parts. So, our customers, when they experience Serval, there's, there's really two agents they work with. One is the admin agent that helps build all of these tools and skills that configure how the help desk, what, what things that the help desk agent can do and what it knows about. And so, it's the admins that build that. And then, there's a help desk agent that end users talk to that resolves their problems. The help desk agent can only use the tools and skills that have been expressly built, published with approvals and permissions and all of that by the admins. And that architecture, that kind of like two-pronged architecture, ends up being really, really powerful

• 10:29Speaker 00

  because you can let the help desk agent run wild, right? Because the end user can ask it anything and it can use its reasoning ability and its full intelligence to be able to solve user problems. But it can only use the tools that the IT admin has expressly said are okay to use.

• 10:46Speaker 01

  Yep.

• 10:46Speaker 00

  And those may have approvals attached, permissions that gate certain users from doing certain things. All of that is done on the admin side, but then you get like kind of the full ability and intelligence of the help desk agent to use those tools appropriately.

• 10:58Speaker 01

  What's under the hood? Is there anything you can say about which models you guys are having luck with and maybe how that's evolved over time?

• 11:05Speaker 00

  Yeah, we, we use OpenAI and Anthropic models today, always experimenting with the latest models from all providers to make sure that we're on the cutting edge. Running eval suites on, on everything that we do. Today, it's still OpenAI and Anthropic models. We find different models are better at different applications. So for the interaction with the end user, we're seeing the most luck with OpenAI models. And that has remained consistent for quite a long time. That, you know, actually calling the correct tools and responding to user in the appropriate way. That's still, we're having a lot of success with, with various GPT models and, you know, always keeping those up to date depending on, on the latest release.

• 11:48Speaker 00

  But on the automation side, the, which is mostly code gen automation, having the most success with Anthropic models. So.

• 11:54Speaker 01

  Interesting.

• 11:55Speaker 00

  Continue to use those, Sonnet, Opus. And tons of trade offs between the different models. And I think what's interesting in recent times is, is the new releases oftentimes are not just like plug and play. You know, sometimes you get some advantages and some things that were working really well don't work as well anymore. So that's become an interesting challenge as things go.

• 12:14Speaker 01

  Actually, yeah, how do you guys manage that? Like, how long does it take to incorporate new models into the production version of your product? How much of that process is like automated in some fashion? How much of that is somebody just has to sit down and figure it out? Like, how, how do you manage that?

• 12:27Speaker 00

  It's not as automated as I'd like. We, we have the evals automated, but then in every situation, when we, when we have a new model that we're testing, some things get better and some things get worse. And a lot of the things that get worse, not necessarily the model got worse, but we built a lot of. Prompt tuning and a lot of infrastructure around the known quirks or behaviors of that model. And those make less and less sense when the new model comes out or where you're swapping models. And so that's where a lot of the adjustments have to be made. And then you kind of run it through the evals and then you do a slow release across customers. So we're getting better at this, but I think there's, there's certain cases where the trade offs have not been worth it. Where we've actually upgraded models and then downgraded the models and said, you know what? The old models are maybe they're a little bit faster.

• 13:11Speaker 00

  Or maybe they're reliable in a way that the new models are. And so maybe the new models are a little bit smarter, but they misbehave in ways that are less predictable. And we have less predictable guardrails to prevent against. And so we're like, hey, this model might not be as smart, but we know it's going to behave the right way for these customers.

• 13:27Speaker 01

  Yes.

• 13:29Speaker 00

  It's been an interesting challenge and it's changed over time.

• 13:31Speaker 01

  How much are you guys factoring cost into the equation right now? Because I think, you know, step one, you got to make sure the product does something magical.

• 13:39Speaker 00

  Yeah.

• 13:40Speaker 01

  Step two, okay, now let's make sure we got a business.

• 13:42Speaker 00

  Yep, exactly.

• 13:43Speaker 01

  And we can, you know, we can extract appropriate amount of value for, for this functionality. Do you guys think much about cost at this point? Or we kind of know where that's going over time. Let's make sure the product is magical and we'll figure out the cost element later.

• 13:56Speaker 00

  It's the latter. And I think one of the reasons that give us this flexibility is that our unit economics end up looking much better than a lot of AI companies. Because we are not in the business of reselling tokens. The way that our product works is that you build these automations, which are, you know, basically types scripts. And once they're built, you don't have to rebuild that. And so every time the end user asks for a password reset, it's not going and regenerating code to reset a password. It's actually just running the code that's already been generated. Over time, users have to generate less and less actual code because we have a growing library of automations that cover the very long tail of things that you might want to do.

• 14:37Speaker 00

  And so there's not as much, especially in the, the kind of very expensive code gen, token consumption. There's not as much as you'd expect. And so the economics are very strong, even though we haven't done a lot of optimizations around that. So I tried to tell the team, spend more money, use the best possible product.

• 14:54Speaker 01

  Yeah.

• 14:54Speaker 00

  We know long-term where this is going. We know that there are all kinds of optimizations we can make down the line. So that's been our focus to date. I think that though where it starts to get more interesting is as we explore more and more applications of like background agents, long-running agents that are not just responding to help desk requests or not just building quick scripts for you, but investigating all of your historical tickets. Or investigating logs from devices and doing all this work in the background and maybe generating solutions to problems you didn't know you had. That's where it becomes a little bit more interesting, where maybe the costs become more relevant. So that's where we'll probably start to think about costs a little bit earlier in the journey. Because those could run away pretty quickly if you're not keeping an eye on it.

• 15:36Speaker 01

  Yeah, that makes sense. Let's say somebody at OpenAI or Anthropic wakes up tomorrow and they're like, wait a minute. I found this company called ServiceNow. And it seems to be like a major system of record, major center of gravity inside the enterprise. I think we should build as a first-party product, you know, the OpenAI ServiceNow or the Anthropic ServiceNow. If they set their sights on you and come after you directly or come after your category directly, what would that mean for Serval?

• 16:04Speaker 00

  Yeah, I mean, this is always a really tough question because on the one hand, any response to like, okay, this company with infinite money and the best engineering talent and AGI wants to do what you do. How are you better? It's kind of like any response to that is going to sound pretty naive of like, well, we're just going to like beat them. But I think the history of startups tells us that often the smaller company does beat them. I mean, the existence of OpenAI and Anthropic are kind of proof that you can beat the entrenched incumbent with infinite resources. And I think it comes down to, you know, maybe divine providence favoring startups or maybe like a lack of focus

• 16:45Speaker 00

  is often what makes it hard to execute. And so, you know, when I was starting my first company in the hole, you know, every EVC would ask me, well, Google just do this. And yes, Google will do a lot of those things, but it actually is very hard to do a lot of different things really, really well. And it's hard to divert your focus into all these categories. And I don't think ITSM makes the most sense as a focus area. One reason is that I think in the past couple of months, Anthropic has added more. ARR than ServiceNow has in the past 20 years.

• 17:13Speaker 01

  Good point.

• 17:13Speaker 00

  And so, does it really make sense for them to take their best and brightest people to throw them at this problem? That even if they're very successful, would not be really, it would take them years to get what they could get out of the rest of their product portfolio in a matter of months. And so I don't think they're going, they're probably look at this category. I wouldn't be surprised if they, they built some kind of simple version, maybe a more mid-market or SMB focused version. But to really devote the time and energy to, to master the complexities of enterprise service management, not to say they couldn't. But the focus that would require, I think would be a bad use and bad prioritization of their resources. And I don't think that that's gonna happen.

• 17:48Speaker 01

  Yeah, I think you're right. Um, let's talk about your customers for a minute. So you got a bunch of the kind of really nice AI native logos. And you're also starting to have some kind of big enterprises. How did the needs differ from the AI native crowd to the big enterprise crowd? And if you had to pick from each of those, what's the nicest thing that they would have to say about you? You know, Serval is amazing because what would they say?

• 18:15Speaker 00

  Yeah, I think what's been the biggest learning is how similar they are relative to how different. I expected them to be much more different. But the pain points and the problems end up looking remarkably alike from the AI native to the, the large enterprises. You know, we work with companies as small as a few hundred employees, up to companies as large as a few hundred thousand employees. The difference ends up being how many people it takes to make a decision. And that's what actually makes things really challenging is because.

• 18:40Speaker 01

  More of a go to market thing.

• 18:42Speaker 00

  It's a go to market, it's an implementation thing more than that.

• 18:44Speaker 01

  Yeah.

• 18:45Speaker 00

  So when you're, if you're working with a company with a few hundred employees, there's probably an IT leader that can say, this is how we're going to do things. This is how onboarding works, this is how we're going to reset passwords, whatever. If you're working with a company with a few hundred thousand employees, no one even knows who that person is, if that person exists. And so you end up in all these kind of committees trying to figure out what should we do here? And that's actually what makes it very, very challenging. Um, and I think you see that in, in these labs, building out these consulting businesses and more and more services and deployment resources. Because that ends up being a lot of the, the rate limiting step in adoption is coordinating all these folks. So what would the nice things they say about me? I, I think, uh, or about Serval.

• 19:26Speaker 00

  In the, the AI native early stage companies, we, we often just, we take an IT person that's passionate about technology and we let them spend their time building. They got into IT because they love technology. And so much of their job before Serval, they weren't really getting to experience technology. My favorite example of this is a customer we had that spent a lot of their day fielding ServiceNow requests to provision someone's access to Cursor.

• 19:51Speaker 01

  Yeah.

• 19:52Speaker 00

  And that juxtaposition was just so sad. I'm like, I am in this like ancient ticketing system, helping somebody else at the company get access to a really cool AI tool. And my tools are still stuck in the past and I don't get to use the cool stuff. And with Serval, they get to use the cool stuff. Like there's actually an AI tool for IT built for them. And so that's, I think, what we see on the native side. I think in the large enterprise, you know, generally they're thinking about it at just a higher level of business transformation. And we hear a lot more about the end employee experience because at a small company, even if your IT processes aren't perfect, no one's waiting weeks to get a response back from IT. But a larger organization, like you could send a ticket into, you know, into the abyss.

• 20:37Speaker 01

  Yeah.

• 20:37Speaker 00

  And, and just have no idea where it's at, if it's being worked on. So then you'll send another ticket and then there's confusion on the other side because now you've got two tickets from the same person. And, and there's actually people that are blocked for weeks.

• 20:48Speaker 01

  Yeah.

• 20:48Speaker 00

  From getting back to the thing they're trying to do. And so I think in a larger enterprise, we actually change the employee experience and what it feels like to be an employee and, and have this more broad impact because the problems are actually a lot deeper in those big organizations.

• 21:02Speaker 01

  What is a delightful or a surprising use of Serval at Serval? How do you guys use it?

• 21:08Speaker 00

  Oh, man, I forced the team to use it for everything. So every time.

• 21:12Speaker 01

  So where do they really push the boundaries of like?

• 21:14Speaker 00

  Yes, exactly. I mean, obviously like things like office requests, like snacks, like all of that has to go through Serval. But I think one of the coolest things we do for Serval is one, we have a channel called Dream Team Draft. We take this very person-first approach to recruiting where we want to identify the best people in the world and we want to bring them to Serval versus just cast a wide funnel, host an open tryout and see who makes it. And so people put the best people they've ever worked with in this channel, they post the LinkedIn. That's a Serval channel. So Serval will take that profile and then run a series of automations. One, it'll, it'll run into all of our outbound campaigns, our nurture campaigns. It'll also do a lot of things that my marketing team won't let me talk about of like making sure that they are seeing Serval everywhere they go.

• 21:58Speaker 00

  And Serval becomes very top of mind for them. And we basically warm this audience to make sure that they know about Serval. And we're playing the long game because these are generally not people that are going to leave their job tomorrow, but people that we'd love to work with one day. And so I love the idea of going in and saying like, hey, all I have to do as an employee is just say, I love this person, they're great and I'm done. And the talent team will be able to, through Serval, have all these automations that kind of get them into the system.

• 22:24Speaker 01

  That is very cool. What's the best reason to work at Serval? Like, why do people join your team?

• 22:30Speaker 00

  I think it is the greatest group of people I've ever worked with. I think when you walk into our office and you meet our team, it's just a group of so, you know, people that are so kind and so talented and so fun to be around. And I think that that's what's really unique is, I didn't even know I was selecting for this when we started the company. But something candidate started telling me is that, wow, your team is so nice. And they're so fun to be around. And like, I walked in the office and the energy was just contagious and I wanted to work there. And obviously, there's really interesting technical problems, you know, building these really complex enterprise automations, getting to touch HR, legal, finance, IT, security, like it's kind of a training ground. We often think about this as a training ground for anyone who wants to do anything in AI.

• 23:13Speaker 00

  You get to like touch all these different departments, build all these cool workflows.

• 23:18Speaker 00

  But I think the, you know, if I'm being honest, the reason you joined Serval is because you meet the team and you realize this is the place for you.

• 23:24Speaker 01

  Yeah, very cool. We heard some of the words that you use to describe your culture, you know, fun and nice and high-energy people. What is it not?

• 23:33Speaker 00

  It is not a good place for, let's say, a lot of training or mentorship. So everyone is kind, but everyone's doing their job. And there is not a lot of, hey, you're going to be on-boarded through this program and you're going to learn how to do these things and we're going to train you really well. And we're going to pair you with, you know, some resources and coaching and mentorship. And there are great companies that do that really well. We are not one of them. So we hire people to come in and basically be productive on day one.

• 24:03Speaker 00

  And who would like that ambiguity of I've got to show up and figure out what I'm supposed to do and then do it really, really well. So it's it's not a place for coaching mentorship. It's not a place for very clearly defined career paths. We don't know where a lot of these functions are going to go. So there's not going to be this clear progression up the org chart. There's not really an org chart. I don't actually know who reports to me at the company. I I am like blissfully unaware of who technically reports to me versus other people because we try to keep everything as flat as possible. And and so if you're looking for that kind of like natural progression, you know, it's just not going to be a good place for you.

• 24:37Speaker 01

  Yeah. As far as one of the big topics that we've had a lot of conversations on recently, just amongst founders and folks, is this idea of living in the future and not only the product that you build needs to be AI native, but the way that you build it and the way that you manage your organization needs to be AI native. Beyond using Serval itself, like what does that mean for you guys? Like, how is the way that you operate changed this time around versus Verkata or your prior companies?

• 25:10Speaker 00

  In so many ways. So one is we are questioning everyone's role. Every department, we're wondering if it needs to exist anymore and if it still makes sense. And oftentimes it does. We relearn the necessity of some departments that we thought maybe we didn't need. And but we start with the the assumption that maybe this doesn't need a person anymore. Maybe this could be AI. So AI almost gets like the right of first refusal for every job or every department of like, hey, maybe we don't need this at all. And maybe this can be much smaller than maybe this.

• 25:38Speaker 01

  Is there a good example of something that fully went to AI?

• 25:42Speaker 00

  Solutions engineering. So we don't have SEs. We also don't have SDRs, but I think that's been that's less of a controversial take over the past few years. But we don't have SDRs and we don't have a solution engineers. Our reps are not necessarily more technical than than reps in in past generations have been, but they have access to Serval.

• 26:01Speaker 01

  Yeah.

• 26:01Speaker 00

  And so any question they have about how the product works or that a customer comes up with a prospect, Serval is going to give them an instant answer to that question. Serval can even build them decks, build them one pagers and quick, you know, battle cards and comparison sheets, like all of that on the fly in the middle of a call. So we expect more out of our AEs and they're not going to have the SE resource. We do have four deployed engineers that assist with the actual pilot implementation. But that's been one example where we didn't need that. We also delayed hiring in a lot of domains, like in, and sometimes not by choice, just by the slowness of our hiring. But we've discovered how far we can get.

• 26:39Speaker 00

  On the enablement side, we have someone in product marketing who's done an incredible job of like building out enablement and all these automated resources and scaled that quite far. And we definitely need more help there. But, you know, a lot of times you get further than you think with these AI tools. RevOps is another one where we've gotten pretty far without a rev up hire, but then discovering like, actually you do need somebody eventually. And so there's a lot of these things where you delay it a little bit and you're like, actually, we need somebody, but maybe this department is a little bit smaller than we thought it was going to be.

• 27:06Speaker 01

  Company is about two and a half years old, thereabouts?

• 27:08Speaker 00

  Just two years old.

• 27:09Speaker 01

  Two years old, okay. And you guys have grown like crazy, more people, more customers, all that good stuff. How has your job changed in the last two years?

• 27:17Speaker 00

  In the very early days, I felt very useless, honestly. My CTO is building the product and it's very clear what we need to build. There's so much to build and I'm trying desperately to hire and to set up customer calls and maybe try to sell this thing and it's not working. And so I think in the early days, it was kind of like trying to figure out how I can be most useful when we don't really have a business yet. And then over time it switches and it becomes a business and stops being me kind of pushing the boulder up the hill and more of the business dragging me along. I think what hasn't changed, I'm still very involved with customers, both in the sales side, but also in like the long-term success and talking to customers every day.

• 28:03Speaker 00

  I'm still very involved in recruiting.

• 28:06Speaker 00

  What I'm not as involved in that I'd like to be is a lot of product. In the early days, I am thinking nonstop about the product direction. And now it feels like the product direction is kind of emerging from our customers through our four deployed engineers and going right into the product. And which is in many ways, great, because you have this kind of nice closed loop of customers talking to four deployed engineers. And then the product's getting implemented and getting better all the time. You know, I've often referred to this as like gradient descent for product improvements.

• 28:32Speaker 01

  Yeah.

• 28:33Speaker 00

  Our four deployed engineers are just swarmed with all these, you know, all this feedback from customers. And they're like, oh, I'll fix this. I'll make this better. I'll change this. And like you fast forward a week and like, wow, the product is a lot better than it was a week ago and it keeps going that way. But I get to spend a lot less time thinking about the future and where we want to be. I think that's something that I need to spend more time on because this stuff is changing so quick. To your question earlier about, you know, how are we different as an organization and AI native organization? I think a big difference is you have to be willing to reinvent yourself so much faster.

• 29:08Speaker 01

  Yeah.

• 29:09Speaker 00

  And disrupt yourself so much faster. Like, we are thinking about just uprooting things that we were convinced were true months ago and going in completely different directions and all these different ways. And we have to have that flexibility. We're going to be renaming parts of the product. We're going to be completely shifting how we do certain things in the product. And we have to be willing to do that over and over and over again. And there's going to be less of this idea of like, I am building a software that's going to last for 20 years. And more, I'm going to build software that hopefully will last six months and then I might have to rebuild it once the paradigm shift and the markets have changed.

• 29:42Speaker 01

  Yeah, and let me ask you about that because there's a foundation model on one side or a set of foundation models and capabilities. There's a customer on the other and there's Serval in the middle. And these capabilities are changing at a very rapid pace. This customer is probably not changing all that fast. And so you guys in the middle are kind of this buffer.

• 30:06Speaker 00

  Yes.

• 30:06Speaker 01

  That is trying to take all these capabilities and put them to work for the customer. So the question is really, it's kind of a change management question. Like, how do you keep the customer from drowning on this downpour of capabilities coming out of the foundation models?

• 30:22Speaker 00

  I think that's exactly the right way to think about it. We are in that we are kind of that translation layer.

• 30:27Speaker 01

  Yeah.

• 30:28Speaker 00

  And we have to meet customers where they are. I mean, I think we very much have to understand their business problems. And that's what really helps with the four deployed engineers is we are starting with their business problems. What are they trying to solve for? And then we are helping them discover how those solutions are implemented in Serval. We are on the other side figuring out how to take in the latest advances in AI to be able to deliver those solutions. And so that is kind of what role we serve. And then we're often educating them of like, hey, here's how we do this. And by the way, it's changed since how we would have done this three months ago. And we have these new tools at play, but we will be the ones to help figure this out for you. And one of the things we're working on is how can we bridge that gap a little bit more succinctly? So how can we make, you know, an agent available to the end user? That kind of says, okay, what are your business problems? Cool.

• 31:13Speaker 00

  Here's what we're going to do. Here's we're going to solve them. So you don't really have to think about the latest advances. Serval just kind of takes care of that for you and you just have to focus on what are your problems? What are you trying to achieve?

• 31:22Speaker 01

  What's your most contrarian take on the world of AI?

• 31:25Speaker 00

  So one take that I have, which I don't know how contrarian this is, but I think there's this big gap emerging between who wants autonomy? And who wants control of these agents? And the individuals in an enterprise, they want autonomy. They want their Claude agent to do everything for them and have access to everything. The organization itself doesn't want their employees, agents to have all this autonomy. And there is this interesting tension emerging between, and I often see this in consumer versus enterprise products where the consumer products obviously are built for a world where you want it to do everything and the enterprise products are built more with this control layer. And what's happening in the enterprise is that

• 32:09Speaker 00

  the individuals are adopting these tools and wanting it to be able to do more. And the, the security organization, the IT organization is very worried about this and for good reason. And I think that's something interesting to track is this tension between autonomy, like do organizations actually want autonomy? Or do just individuals want autonomy? And how do you navigate that tension? And that's where we're thinking a lot about the serval product and how we help solve that.

• 32:35Speaker 01

  It reminds me of shadow IT back in the day when consumers started to adopt iPhones. And enterprises still had BlackBerrys because they could lock down the BlackBerry. And the iPhone started to show up with work information and then work applications. And they're kind of sneaking their way into the enterprise and the instinct of most. CISOs was, you know, protect, protect, protect. And then I think eventually they realized, well, wait a minute. This is kind of the leading indicator on what my employees need to be productive. And they started to embrace it.

• 33:06Speaker 00

  Yep.

• 33:07Speaker 01

  Right? And it feels like the same thing's happening now with AI where employees kind of, a lot of them kind of know what they need to be productive. And as long as you can just kind of systematically follow those signals and embrace it, you're more likely to end up on the right side of history.

• 33:22Speaker 00

  Yeah, and I think the companies that that basically say yes as the default are going to be way ahead of the companies that say no as a default. And we're seeing this with the companies that just embrace it. They will also kind of be leading the charge and face a lot of the consequences of there are going to be security incidents, there are going to be problems. And and we're going to learn a lot from those. But I think that that is, that is what we're going to see is the companies that take those risks are going to get ahead.

• 33:48Speaker 01

  Yeah.

• 33:48Speaker 00

  But, you know, those risks are real. And they're also going to be some of the ones that suffer some of the consequences on the security incidents.

• 33:54Speaker 01

  What's your biggest issue at the moment?

• 33:56Speaker 00

  Our biggest issue is hiring still. Every AI company I talk to is is in the same situation. Even though AI is allegedly supposed to automate all this work and take away all these jobs, we're all still hiring more than ever before. And it's still like our number one priority and our number one concern. And so, but I think people remain the biggest moat you can have. And having better people in the room is really the only thing that will keep you ahead of the competition. But it's the only moat that's left is the people in your organization. So hiring and scaling and keeping the bar incredibly high as we hire are the things that keep me up at night and and worry me, especially as the business grows so much that I have, I'm less involved. You know, there's a lot of people that I will only meet in their interview and they're onboarding,

• 34:39Speaker 00

  but not have probably a lot of one on one interactions after that.

• 34:42Speaker 01

  Yeah, the talent density point, I think, is a good one. And we think about that a lot, too, because we're sort of in this world where cost goes down, capabilities go up. The result is accelerating change. If the world is changing quickly around you, you have to optimize for agility.

• 34:56Speaker 00

  Yes.

• 34:56Speaker 01

  The best way to optimize for agility as an organization is to have the smallest possible number of the best possible people. Right? And so I feel like the returns to talent density have never been higher than they are today. And so like you guys could go hire a million people, but like doing so with the sort of quality and culture, culture fit that you need, you know, that's, that's tricky.

• 35:17Speaker 00

  And to your point, it makes it harder to steer the, to turn the ship.

• 35:20Speaker 01

  Yeah.

• 35:20Speaker 00

  And so our mantra on hiring is fewer, better, fewer, better. Like we say this over and over again, fewer, better. Like, how can we make this department fewer and better?

• 35:27Speaker 01

  Yeah.

• 35:28Speaker 00

  And, and that's going to be really important because again, I think we're going to have to reinvent ourselves more frequently than companies have ever had to do this before. And I can imagine a, a future version of Serval where it's unrecognizable from what we do today because we just had to adapt so quickly. And that's much easier to do with a small agile team than if we scale very rapidly, you become thousands and thousands of employees and you say, hey, by the way, we're changing everything about our go to market. We're changing everything about what our product does.

• 35:52Speaker 01

  Yeah.

• 35:53Speaker 00

  You know, we're making some big changes to the product and it's nice to have a relatively small organization that can embrace that shift almost overnight. I can't imagine what we do if we had to convince thousands of people that this is the new direction.

• 36:04Speaker 01

  Yeah. Let's assume, and obviously this is not something we can take for granted, but just for fun, let's assume, let's assume that Serval becomes a monster company, you know, gazillion customers and employees and revenue and market cap and free cash flow and all that great stuff. Let's assume you're a monster company. At that point, what would you want people to say about you? What else would you want to be true that is not contained in one of those traditional metrics of scale or success?

• 36:32Speaker 00

  I think I would want the impact of Serval to be very clear in terms of like what it did for the world. The impact I think is most important in what we do today is, is we unlock meaningful work for people.

• 36:44Speaker 00

  You know, I think that there's always a gap between the idealized vision of what you think your job's going to be and then what your job actually is. I think it's true for every profession. You idealize, and kids do this the most, right? When they're like, want to be a firefighter, an astronaut. What that job looks like. And then you get in that job and you realize, oh, there's actually a lot in this that I don't like. And we want to be the tool that actually closes the gap between what you think your job's going to be and what your job actually is. And we do that by automating away all this, you know, repetitive, menial work that you don't want to do and that is not productive and is not part of the reason you took this job. And we've done that in many ways for IT. And I think as we unlock automation for the entire organization, that's what we do for

• 37:27Speaker 00

  people, is we get them back to the work that they actually signed up for and the work they actually enjoy. And so I would hope that there is clarity that that was the impact that we had.

• 37:36Speaker 01

  Yeah.

• 37:36Speaker 00

  And it wasn't just like, oh, Serval automated away a bunch of my T jobs. It's like, no, I want people to feel like Serval made people's work lives a lot better.

• 37:44Speaker 01

  Awesome. That feels like a great place to end it. Jake, thank you so much for joining us today.

• 37:50Speaker 00

  Thank you so much for having me. This is a blast.