How Warp Went From YC to a $60M Series B

1. 0:00 – 0:50

   Intro

   1. HTHarj Taggar0:00

      [upbeat music] I'm excited to be joined here today by Ayush, founder and CEO of Warp. Warp is an AI-native employee management platform built for high-growth companies. They work with over 1,000 customers and have processed over $600 million in payroll in the past year and are on track to pass $2 billion in the next 12 months. Warp did YC in winter 2023, and they just announced their $60 million Series B led by Battery Ventures with participation from Sapphire, Peak XV, and Shopify CEO Tobi Lütke, and former Stripe COO Claire Hughes Johnson. Today, we're gonna talk about their origin story and what it actually means to be an AI-native product and company. Ayush, thanks so much for being here.

   2. ASAyush Sharma0:49

      Pleasure to be here.

2. 0:50 – 3:10

   From India to MIT

   1. HTHarj Taggar0:50

      So Ayush, I actually want to start, um, by hearing a little bit about your background and how you grew up, 'cause I think you have a very unusual entry into technology and startups, and actually a really inspiring story.

   2. ASAyush Sharma0:59

      Yeah. I grew up in a really small town in India, and in a place where I didn't know a single person who had gone to the US. So there's a fun sort of journey and story there, which the short version basically is that I-- growing up, I was really into physics. So as early as I can remember, I was trying to borrow textbooks from upperclassmen, and I remember, um, finding Richard Feynman's lectures on physics, the, the classic three, three-volume books, right?

   3. HTHarj Taggar1:23

      The easy piece. I know the whole... Not even the easy pieces.

   4. ASAyush Sharma1:25

      The, the, the, the actual one.

   5. HTHarj Taggar1:25

      The full actual-

   6. ASAyush Sharma1:26

      Yes, yes

   7. HTHarj Taggar1:27

      ... the actual Feynman on physics. All right.

   8. ASAyush Sharma1:28

      Actual lectures, the, the one he gave on Berkeley, which was, uh, turned into textbooks later on. Um, and I-- as I was growing up, I-- that was, like, sort of, I, I really thought I would go on to become a theoretical physicist and a professor at one day. Um, and then sort of life came, comes at you, you know, the Indian middle-class family expectations.

   9. HTHarj Taggar1:50

      [chuckles]

   10. ASAyush Sharma1:50

       And I was told that the only way to kind of escape the middle-class sort of fate in life in India, where I was growing was, is you have to become an engineer and go to an IIT, which are these top schools in India.

   11. HTHarj Taggar2:01

       Yeah.

   12. ASAyush Sharma2:01

       And I was, um, doing really well, and I was on a straight track to go to one of the top three schools in India, and halfway through that prep, I just completely sort of pivoted and changed my mind and sort of behind the backs of my parents and family. There's a reason for that I'll get to in a second. But, uh, I ended up applying to MIT, which is where I knew Richard Feynman had went. Uh, so that's the connection. And, um, fortunately, I sort of-- the way it worked out, I got into MIT, I got into Columbia, um, to study computer science, math, physics. Um, so I spent about five years back at MIT, um, specializing in machine learning. But, um, overall, I think, like, the fun kinda statistics that I can share is I think I'm the first person out, out of a state in India that has about 250 million people to get into MIT for undergrad. So it was quite fun. That was about 10 years ago, and now we're here. [chuckles]

   13. HTHarj Taggar2:52

       I remember before Warp, you were sort of trying out different startup ideas and maybe, um, I think I remember some of them in the consumer space.

   14. ASAyush Sharma2:58

       Yeah.

   15. HTHarj Taggar2:58

       So, um, tell us a little bit about that. Like, kind of how did you go about trying to come up with the initial startup ideas and then, um, yeah, why the sort of like the pivot from consumer to, um-

   16. ASAyush Sharma3:09

       Yeah

   17. HTHarj Taggar3:09

       ... payroll

3. 3:10 – 5:18

   Betting on an Unsexy Problem

   1. HTHarj Taggar3:10

      and B2B?

   2. ASAyush Sharma3:10

      Yeah. So at first, you know, I, I was-- I must've been 24, I think, when I was really looking into what to do first. And like most 24-year-olds do, um, you try to start a social app. [chuckles]

   3. HTHarj Taggar3:23

      Yeah.

   4. ASAyush Sharma3:23

      Especially a couple years ago. Um, this is pre, pre-AI, right? Um-

   5. HTHarj Taggar3:27

      Yeah, there's, there's like a roommate finder.

   6. ASAyush Sharma3:29

      [chuckles]

   7. HTHarj Taggar3:29

      There's bill splitting, like events.

   8. ASAyush Sharma3:31

      All, all the Tarpit ideas that YCs made five years back, right?

   9. HTHarj Taggar3:34

      [chuckles] Yeah.

   10. ASAyush Sharma3:35

       Um, but I think I just wanted to build a thing that I found interesting or, like, I would have used. And so I got started on building this app. Um, it was really fun for a couple months, and we had an interesting community and people joining it. It allowed me to get started and just make the leap of, like, we're just gonna build something and see what happens. But at the same time, I think very quickly it became clear that it wouldn't become a really big company. And that's when we sort of had this fork. It was like, okay, um, me and then the early team members that we had at the time, uh, Adam, who's our CTO, we just sit, sat down and we were like, "Okay, what are all the problems that we have faced in, in the past and in doing this that we could come up with?" So kind of really went down and kind of did this very low-pressure exercise of what are all the problems we've actually run into that we think we'd want to solve for ourselves? And one of the things that we kept coming back to was, um, in starting the previous thing, I'd actually had to set up, like, a company, a C corporation, and try to pay some people. And I just remember one of the days that I spent-- I think I had all these fires going on in the company, and then on top of that, I had to, um, figure out how to make, like, a New York withholding and Department of Labor's account. Um, and it was one of the most frustrating things I ever spent my time on. And I was like, "There's no way nobody has com- tried to completely automate this." And, uh, I think the early background I had coming from machine learning, and this is right, like, 2022, '23, when LLMs and agents were just about to take off. And I think it was sort of interesting to consider what if we apply AI and LLMs towards this, and could we automate this in a way that nobody has before? And that's the start

4. 5:18 – 9:49

   The Wedge That Started Warp

   1. ASAyush Sharma5:18

      of the Warp.

   2. HTHarj Taggar5:18

      How did you think about that as a pro-- Like, how excited were you to work on that problem, I guess?

   3. ASAyush Sharma5:22

      Yeah.

   4. HTHarj Taggar5:22

      Like, I, I think big part of the reason why I think especially young technical founders start on sort of consumer ideas is 'cause they're, they're things they wanna use and they're excited and, um, and attention-grabbing. Um, how did you-- Like, what, what motivated you to wanna solve this particular problem when it, on, on surface it might not be as, um-

   5. ASAyush Sharma5:38

      Yeah

   6. HTHarj Taggar5:38

      ... obviously interesting?

   7. ASAyush Sharma5:39

      I think in some ways I was motivated to start it because it is unsexy. And Paul Graham, of course, has this really foundational essay on schlep blindness.

   8. HTHarj Taggar5:48

      Yeah.

   9. ASAyush Sharma5:49

      And I, I remember thinking about that, that so many of these problems Hide in plain sight, and nobody really tries to attack them because they seem really messy, complex, uh, and unsexy at the start. And I think the classic example being the call listents with Stripe, uh, nobody was really sort of like finding it sexy to figure out all the edge cases with million banking APIs that were very janky at the time. Uh, but it needed to be solved, and I think, um... So o-one motivation was simply that this seems like the kind of problem that is very messy, hairy, and not sexy on the surface. But counterpart of that, I think the more we learnt about it, the more we went a little bit looking into it, we found other people who had run into the same problem. So it's like, okay, we're not the only person that is thinking that this is really bad, the current state of the world here. And two, we saw a path to using that towards building a really big platform for all things employee management. And that was sort of the controversial idea at the time, is that we could actually use this multi-state complexity, taxes, deep compliance workflows towards building the entire platform.

   10. HTHarj Taggar6:59

       You're entering a, a pretty competitive space, like lots of... There's legacy payrolls, there, there's recent-ish startups that have done well. Um, uh, that's what the making it easy to run payroll in multiple states was like the initial wedge. Um, how did you know that that was like a, gonna be a sufficiently like deep wedge?

   11. ASAyush Sharma7:18

       So I, I have a funny story here. When, um, we were thinking of applying to YC, I think I DM'd you actually-

   12. HTHarj Taggar7:25

       [chuckles]

   13. ASAyush Sharma7:25

       ... and you encouraged us to apply. And-

   14. HTHarj Taggar7:26

       I don't even, I didn't remember that at all. That's funny. [chuckles]

   15. ASAyush Sharma7:28

       Yeah, yeah. It's, it's been a while, right? Um, but and, and you said, "Yeah, definitely apply." So we wrote an application, then we were invited for an interview, and it was the first batch that Garry Tan had come into YC. [chuckles] And so I, I hop on this interview, and, uh, there's five people on that Zoom call [both chuckling] on the other side, including you, Garry, and a couple of other partners. And I remember the next 10, 12 minutes were just like brutal. Um, really, like I was-- I, I felt like I was being grinded-

   16. HTHarj Taggar7:56

       Being fired

   17. ASAyush Sharma7:56

       ... on the spot. [chuckles] And that's maybe the typical experience, but I really felt that. And, uh, and one of the big questions exactly was this, like how big can you really use this wedge to kind of grow, um, from here? And is it really a true sort of problem and wedge that you can kind of find enough of foothold on? And, um, at the time, I think it was a bit of a hypothesis on our part that like this is a reasonable wedge into this broader segment. And looking back, I think in some ways we got a little bit lucky, I think. But the, the sort of like the hyp- the calculated part of that hypothesis was that all the companies were multi-state very earlier on, as opposed to just a few years ago, before COVID, for example, where that was much rarer. So the trend line was just going up, right? The complexity, um, was exploding. And number two, um, we realized something kind of non-obvious thing that, that YC has funded some sales tax startups, and I think there's a parallel version of this problem in sales tax. But, um, it's actually much worse when you look at payroll taxes, because unlike sales tax, where there's usually these generous thresholds for when you are beholden to those requirements, like you have to reach a certain amount of sales that's quite high, or you have to like enough, enough sales or enough customers in a jurisdiction. But in payroll, you just need one person. You just need to hire one person in one jurisdiction, and you have to comply with the entire tax jurisdiction apparatus. So that kind of thing, it just means like the moment you are scaling company, you just have one person, and combined with thousands of tax jurisdictions, that complexity really ex- explodes very quickly. So those two things, I think it ended up working in our favor that it became enough of a wedge. It was the right time to kind of use that and also apply LLMs and agents to something that traditional software

5. 9:49 – 12:31

   What "AI-Native" Really Means

   1. ASAyush Sharma9:49

      couldn't touch.

   2. HTHarj Taggar9:49

      So as the company's evolved and visions evolved, like your-- you describe yourself now as AI-native employee management, because you do more-

   3. ASAyush Sharma9:55

      Yeah

   4. HTHarj Taggar9:55

      ... than just payroll. But, um, what does that mean exactly? And let's talk about that for just like the product. Like what does it actually mean to be an AI-native product?

   5. ASAyush Sharma10:03

      I think one big realization we've had is that, one, we have to build the whole platform. And it became very clear to us that as our customers were... In many cases, like early on, we had signed up, uh, a lot of startups because they tend to be early adopters. Uh, they were going through the exact like compliance multi-state problems, um, and they didn't have full like HR or legal or accounting teams internally that would try and maybe DIY that stuff. So those early customers, they sort of pushed us as they were growing themselves to just con- gave us the direction of, okay, um, I want, as we're going from like five to 10 to 50 to 100 to 200, the, they kind of gave us the roadmap of what exactly they would need at those different stages of those companies. And they wanted Warp to do all of it. So it became clear to us that we needed to do more than just initial payroll, especially if we wanted to serve these scaling high-growth companies throughout their entire life cycle. And two, I think the bigger maybe realization was we have to build the product and the platform and the architecture in a very AI-native, agent-native way, such that all the work here that we're doing, starting with this very like deep tax compliance workflows, can all be performed by an agent. And that's the kind of, I think, efficiency gain that we're really focused on is if you're a high-growth company, and today we serve tons of really fast-growing AI-native startups and customers like Serval, Bland AI, Greptile, Reducto, and many, many of which are really cool YC companies, of course, but also traditional high-growth companies, mid-market, and now some enterprise customers as well. And I think the pattern that we continue to see is these customers, they are not interested in linearly scaling their people ops, HR ops, finance teams as their company is scaling. And that's where I think we want to be the entire platform for these customers that allows them to do that.

   6. HTHarj Taggar12:07

      So as like you... You're, you're very involved in product and as you're building the product, just again, help people understand When you're thinking about building, like, these new sort of units or product units, what does-- and what does it mean to sort of do that in, like, an AI-native way? Like, what-- maybe, like, give us like-

   7. ASAyush Sharma12:21

      Oh, yeah

   8. HTHarj Taggar12:21

      ...what, what's like the, the-- on the one hand, here's kind of the AI-native way to approach building, like, a, a tax compliance, and here's, like, the, the regular way to do it.

   9. ASAyush Sharma12:29

      Yeah.

   10. HTHarj Taggar12:29

       And help us understand the diff.

6. 12:31 – 14:00

   Building a Different Kind of Company

   1. ASAyush Sharma12:31

      Great question. So a traditional way of building an HR tech company that has compliance requirements baked in, um, that you have to solve for all your customers was purely by headcount. And so one of the things you notice is if you look at our legacy HR incumbents, um, if you look at their headcount and where their headcount is, often, um, it's very surprising that, like, 30% to 40% of their headcount goes in functions like support, tax compliance, operations, um, accounting, legal. And this is relatively similar between all the last generation HR tech companies. Um, in contrast, Warp, um, we serve now over 1,000 customers. We process over $600 million in payroll already, growing quickly. We touch every single tax jurisdiction that exists in America, and there's over 1,000, uh, without going into the super locality municipalities. [chuckles] Um, all 50 states, and we have... Up until recently, we had only one, one and a half tax person. One full-time, one part-time. And now we've hired the second full-time tax person, more because we're anticipating the future growth. But I think that sort of tells you and kind of points the picture of how I think we're able to build a different kind of company, not just a different product. 'Cause I think it's a very different structure of the company itself if you can use AI to automate these previously really messy things that software couldn't touch. Um, and that's why we get really excited about

7. 14:00 – 16:42

   Why AI Favors Technical Founders

   1. ASAyush Sharma14:00

      it.

   2. HTHarj Taggar14:00

      For someone starting, like, an AI-native company, um, you know, the advantage you have against incumbents is y- it's sort of like green field. Like, you can, you can sort of build a new set of primitives and however you want for, like, the world that we live in today.

   3. ASAyush Sharma14:13

      Yeah.

   4. HTHarj Taggar14:13

      On, on the other hand, like, incumbents in any space, they, they build up to, like, institutional knowledge. They've been working with the customers for a long time. Like, a bunch of those, the people there do have, like, sort of, like, experience and expertise. Like, how do you think about sort of your advantages and disadvantages maybe as sort of a, a new, um, a new entrant in a space using sort of like, um, AI-native as the, as the angle?

   5. ASAyush Sharma14:36

      I think that net-net, I believe that with AI, it's in favor of technical founders. And what I mean by that is if you looked at the last era, like pre-AI, sort of the, the late-stage SaaS where it felt like the, the terminal state of software was this, like, SaaS and mega SaaS software. It had gotten quite bor-boring in some ways. I don't think I would've started a company in that era necessarily in this direction. And in that time, I think the advantages were really towards, like, mega distribution, sales-oriented founders. And you kind of see that in the backgrounds of those companies that got started. And I think today, a- and really with AI, it really shifted the power, sort of the balance where younger, more technical founders with deep technical knowledge, uh, of the frontier, they can sort of see where this is headed, and they don't have these ideas and kind of like how things are supposed to be. They can kind of shed those older norms and kind of build something net new. And I do think that is net-net where this is headed right now. But at the same time, I think to your point, like the incumbents with distribution and l- lots of these channels and partners, that exists, and I think it is one of those situations where it's a classic, uh, will the incumbent adopt this new technology in, in this platform shift before the, the new entrants figure out how to become the next generation entrants and big software companies? And I think that as, as a startup founder, my belief in this space is that it is very hard to retrofit AI on top and just kind of layer these sort of thin chatbots on top of existing architecture and existing customer bases and install bases. And in some ways, I think the market's recognizing that. I think if you look at work, something like Workday, which is the, um, the last generation mega enterprise company in our space, uh, it's down 60%, 70% from their current high- from their recent highs. And, um, I, I think that to me is underappreciated

8. 16:42 – 21:25

   The Next Generation of Enterprise Software

   1. ASAyush Sharma16:42

      right now.

   2. HTHarj Taggar16:42

      Something else that, uh, with, in, in general for talking about AI and, uh, its impact on startups that gets mentioned a lot is this concept of systems of record.

   3. ASAyush Sharma16:50

      Yeah.

   4. HTHarj Taggar16:51

      So this idea that, um, you know, you mentioned sort of Workday as an example-

   5. ASAyush Sharma16:54

      Yeah

   6. HTHarj Taggar16:54

      ...late-stage SaaS company. Um, it feels like the belief right now is that at least consensus, conventional wisdom, uh, is a lot of SaaS is not defensible against sort of AI disruption, that there just won't be any need for it. That systems of record will be defensible-

   7. ASAyush Sharma17:10

      Yeah

   8. HTHarj Taggar17:10

      ...that there, those have real moats against sort of, um, agents doing all the work in this AI future.

   9. ASAyush Sharma17:16

      Yeah.

   10. HTHarj Taggar17:16

       Mo- I think Warp would be classified as a system of record. Um, maybe just explain to us, like what, what does that actually mean? When people talk about a system of record, what is that a- and why is that sort of theoretically defensible against, um, the sort of future where AI agents are, are removing the need for software?

   11. ASAyush Sharma17:33

       So a system of record is basically a database where there's some amount of semantic mapping towards a process that the, that business uses. And so in, in very simple terms, storing values, knowing for sure that the values are true, being able to see those histories and logs and all these things that everybody in a company can agree on. That's a system of record.

   12. HTHarj Taggar18:00

       Shared truth.

   13. ASAyush Sharma18:00

       Shared truth. That's a great way to put it. In the, the classic example, of course, is Salesforce, where, um, it is the shared truth for a business for all things that touch sales process. So your CRM is, is in many ways, like I think, uh, we, we actually... A quick anecdote. We, we started and tried out some AI-native platforms For our CRM in the early days. But as soon as we started growing the sales team and went aggressive on that after Series A, the head of sales was like, "We gotta get on Salesforce-

   14. HTHarj Taggar18:32

       Yeah, classic

   15. ASAyush Sharma18:33

       ... because we are missing out all these 20 integrations to all these tools," right? Ve- very classic. I think like many companies have gone through that. I, I do think that there are different system of records have different degrees of like how quickly, how defensible they are. And so far, I've found that like actually Salesforce CRM is actually quite defensible. But in general, I, I think what's interesting is, um, we-- i- there's a bit of a race where the AI native upstarts, can they become the next generation system of records faster than the current incumbent system of records can layer on AI on top of their current systems? [chuckles]

   16. HTHarj Taggar19:16

       Uh, that makes sense. And then, but like, do you have any sense of kind of what, what does that-- what's a next generation system of record? Like, why is that gonna be different? Why won't it be sort of-

   17. ASAyush Sharma19:24

       Yeah

   18. HTHarj Taggar19:24

       ... you know, the essentially just a database with some sort of semantic mapping on top of it?

   19. ASAyush Sharma19:29

       The, the reason I think a company like Workday is really down and suffering is because the worry is that they are at, at risk of becoming a database-only company. And, um, in not, not in a good sense where like, yes, they're a ClickHouse, for example-

   20. HTHarj Taggar19:43

       Yeah

   21. ASAyush Sharma19:43

       ... is a lovely database company and growing really quickly and so on. But more so that it's a dumb data store that agents from external parties just access to do the work. And then if that's the case, all the value moves to the layer that's orchestrating the work with agents. So I think the next layer that we are excited about building is, is effectively what is the in systems of intelligence, not just systems of record, where agents can natively act on the underlying database with that shared truth in mind, with proper guardrails, workflows, permissioning, controls. But it all has to sort of map out in a way that I think the underlying system of record combined with the agents is where I think the interesting stuff's gonna happen and what will be defensible. 'Cause I, I-- there's an interesting chart, I think, uh, Mintlify, for example, was talking about this, a cool company, that most of the API docs are now being-

   22. HTHarj Taggar20:44

       Yeah

   23. ASAyush Sharma20:44

       ... read by agents.

   24. HTHarj Taggar20:45

       Yeah.

   25. ASAyush Sharma20:45

       And I fully believe that I think most of the internal systems and like system of records will be swarmed by these agents.

   26. HTHarj Taggar20:53

       Yeah. I think Cloudflare, I think, uh, it was a couple of weeks ago announced that like, you know, total, um, agent traffic, like to- total web traffic from agents surpassed human web traffic-

   27. ASAyush Sharma21:01

       Yeah

   28. HTHarj Taggar21:01

       ... for the first time.

   29. ASAyush Sharma21:02

       And I think we've yet to see true enterprises and true enterprise software become, like that flippening is yet to happen, I think. But it will happen, and I think what Warp wants to do is build that version, that super powerful, capable system of record platform that is on d- by day one, from day one, built with intelligence in mind.

9. 21:25 – 25:11

   Why Investors Backed Warp

   1. HTHarj Taggar21:25

      Make something agents want.

   2. ASAyush Sharma21:26

      Yeah. [chuckles]

   3. HTHarj Taggar21:26

      It's like basically the, the mantra around here.

   4. ASAyush Sharma21:29

      Yeah.

   5. HTHarj Taggar21:29

      Okay. So let's talk a little bit about, uh, the Series B. You just raised a big round, $60 million. It happened really quickly, actually, right? So tell us, um, what did sort of Battery see that made them move with like such high conviction and, um, and make such a big bet on you?

   6. ASAyush Sharma21:43

      Well, ultimately, you'd have to ask Battery.

   7. HTHarj Taggar21:44

      [chuckles]

   8. ASAyush Sharma21:45

      But [chuckles] I think the, from where I see it, um, we ended up getting preempted. This was just, uh, less than 10 months after our Series A. Um, I think a, an interesting shift happened in the last couple of months where it-- we went from not thinking about what is the AI native future here look like to sort of appreciating this, that if you look at the true enterprise software categories, like for example, CRM, there's a number of AI native challengers. Um, some are growing really quickly. If you look at something like ITSM, um, Servol, who's now a customer, um, is doing really well and growing quickly, and a couple of other companies. Then ERP, there's Relate, Campfire, which is a YC company-

   9. HTHarj Taggar22:28

      Yeah

   10. ASAyush Sharma22:28

       ... um, as well. But I think ultimately one of the last categories remaining there where there isn't a clear sense of what comes after or like these, a true AI native challenger is AI native HCM. And I think that's where Warp comes in. And I, I do think that the reason the round happened really quickly, um, yes, we've been shipping a lot. We've built out a really cool platform. Um, and our customer growth has been amazing. We've signed some awesome customers. But I think when it comes to investors, I do think these tend sort of, there's a little bit of that narrative flip that-

   11. HTHarj Taggar23:01

       Yeah

   12. ASAyush Sharma23:01

       ... happens in certain times where, um, it goes from very, very like far-fetched to it's like you can see that maybe this is the future. And I think that people started feeling that a little bit more. My-- that's my guess of why it happened very quickly. [chuckles]

   13. HTHarj Taggar23:15

       You've, you've got a bunch of money now. So what, uh, what do you plan to do with it? What kind of, um, what can you do now that you couldn't do before? And then what are some of the, um, things you're personally most excited about on the product roadmap for Warp?

   14. ASAyush Sharma23:28

       So now that we have 60 million, um, and more actually from most of the funds we had from our Series A as well-

   15. HTHarj Taggar23:34

       Nice

   16. ASAyush Sharma23:34

       ... we decided to raise a little bit of a bigger round because I think this next phase of where we're about to head, there's two really big and important things we have to accomplish. Number one is we have to build an incredibly engineered, really powerful, and still very usable, delightful platform, um, for all things employee management, which means there's multiple product lines, there's IT, there's benefits brokerage. We became fully licensed countrywide to broker healthcare, medical, dental, vision, 401 (k) s, life insurance, et cetera. Um, we have built out our own device management software, one of the only-- Well, we're, besides Rippling, one of the only companies that has done that, uh, in-house ourselves. We have built out global contractor products, um, uh, offer letters, traditional HRIS things, and so on, right? So there's quite a bit of breadth in the platform, and we have to keep building those with all the integrations to other software, then ERPs and things like that. But- In parallel, we also have to build the agents and the infrastructure for AI to natively act on all of it, on all employee data that's in Warp. And so that means the in- the most capable agent harness that can reliably and with tr- user trust act on their very sensitive tax, payroll, employee data. And so those two things, we don't see them as being able to pick one or the other. I think we just have to do both, and that's where, um, I think this bigger round helps us to kind of catapult into building the engineering team, the platform, the product for that next phase.

10. 25:11 – 26:57

    The Future of Employee Management

    1. HTHarj Taggar25:11

       Uh, any sort of specific things in that w- within all of that, that you're, you're personally really excited about?

    2. ASAyush Sharma25:15

       We're about to launch in the next couple of weeks, um, full GA release of the Warp customer agent. And what that means is, um, historically, we tended to use AI in the back- as background agents a lot to automate workflows on taxes and compliance and filings, uh, and restrictions a lot in the background. But we've built enough infrastructure now to give all of that to the customer's hand as well. And so what I mean by that is anything that you can do in Warp, uh, by clicking buttons, uh, period, you can now do with the agent. So the advantage of that is going to be to be able to very easily and very efficiently create these more complex workflows, such as like when an employee joins in New York, uh, pr- add them to this, uh, healthcare plan, create, add them to this sick leave policy that is New York compliant, and provision their devices and apps and, um, make sure that they complete the New York security training and so on and so forth, right? So all of that, you can just script that via natural language, not some like really complicated code like builder, which historically is how this was done, and then, uh, be able to do that very, very, very efficiently without having to know how to code. And I think that is what I'm personally very excited about. I think we're seeing some interesting use cases emerge from our top customers already, so very excited to do that for all our customers soon.

    3. HTHarj Taggar26:37

       Great. Okay, well, Ayush, I think that's all we have time for today. Congratulations again on raising the round, and I'm really excited to see where Warp goes from here.

    4. ASAyush Sharma26:44

       It's been great to have you as a partner and YC on this journey.

    5. HTHarj Taggar26:47

       [laughs]

    6. ASAyush Sharma26:47

       So, um, thank you so much for having me. [outro music]

Episode duration: 26:58