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

I'm excited to be joined here today by Aush, founder and CEO of Warp. Warp is

an AI native employee management platform built for high- growth companies. They work with over 1,000

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 Toby Luki and former Stripe COO Cla Hughes Johnson.

Today we're going to talk about their origin story and what it actually means to be an AI native product and company.

Aish, thanks so much for being here.

Pleasure to be here. So are you I actually want to start um by hearing a little bit about your background, how you grew up because I think you have a very unusual entry into technology and stars and actually really inspiring story.

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

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 upper classmen and I

remember um finding Richard Feineman's lectures on physics. The the classic three three volume the full actual actual on physics 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 know the

Indian middle class family expectations 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 up was is you have to become an engineer

and go to an IIT which are these top schools in India. 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

family. There's a reason for that I'll get to in a second but I ended up applying to MIT which is where I knew Richard Fineman had went. Uh so that's the connection and um fortunately I sort

of the way worked out I got into MIT I got into Colombia 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 kind of 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.

I remember before warp you were trying out different startup ideas and maybe um I think I remember some of them in the consumer space. So um tell us a little

consumer space. 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

payroll and B2B.

Yeah. So, at first, you know, I I was I must have 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.

Yeah. Especially a couple years ago. Um,

this pre prei, right?

Yeah. There's like a roommate finder, there's bill splitting, like events, all the carpet ideas that my kids made five videos, right? Yeah.

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 in 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

it. It allowed me to get started and just make the leap of like we're just going to build something and see what happens. But at the same time, I think

happens. But at the same time, I think very quickly 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

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 s 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 would want to solve for ourselves and one of the things that we kept coming back to was um in starting the previous thing I' actually had to set up like a company a CO cororation and try

to pay some people. And I just remember one of the days that I spent the guy 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 in Department of Labor's account. Um and it was one of the most

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

on. And I was like, there's no way nobody has tried to completely automate this. And uh I think the early

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 LMS towards this and could we automate this in a way that nobody has before and that's the start of the war.

How did you think about that as a problem? Like how excited were you to

problem? Like how excited were you to work on that problem? I guess like I think big part of the reason why I think especially young technical founders start on consumer ideas is because the things they want to use and they're

excited and um and attention grabbing.

Um how did you like what motivated you to want to solve this particular problem when it on surface it might not be as um obviously interesting? I think in some

obviously interesting? 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

schle blindness. And I I remember

schle blindness. 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

the start. And I think the classic example being the Colison 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 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 to that I think

surface. But counterpart to that I think the more we learned 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

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. you're entering a pretty competitive space like lots of there's legacy payrolls there's

recentish startups that have done well um that's the making it easy to run payroll in multiple states was like the initial wedge um how did you know that that was like going to be a sufficiently

like deep wedge so I I have a funny story here when um we were thinking of applying to YC I think I DM'd you actually and you encouraged us to apply I didn't I didn't remember that at all

that's yeah It's been a while, right?

Um, but and and you said, "Yeah, definitely apply." So, we wrote an

definitely apply." So, we wrote an application, then we were invited for an interview, and it was the first batch that Gary Tan had come into. And so, I I

hop on this interview, and there's five people on that Zoom call on the other side, including you, Gary, and a couple of other partners. And I remember the next 10, 12 minutes were just like

brutal. Um, really like I was I felt

brutal. Um, really like I was I felt like I was being grinded on the spot.

And that's maybe the typical experience, but I really felt that. And uh 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

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 h 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 co for example where that was much rarer. So the trend line was just

much rarer. So the trend line was just going up right the complexity um was exploding. And number two, um, we

exploding. And number two, um, we realized something kind of a non-obvious thing 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

jurisdiction apparatus. So that kind of thing it just means like the moment you're scaling company you just have one person and combined with thousands of tax jurisdictions that complexity really

explodes very quickly. So those two things I think it ended up working in our favor that it became enough of a wedge the right time to kind of use that and also apply LLM and agents to

something that traditional software couldn't touch.

So as a company's involved and divisions involved like you're you describe yourself now as AI native employee management because you do more 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? I think one big realization

product? 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 gave us the direction of okay um I want as we're going from like 5 to 10 to 50 to 100 to 100 the

they kind of gave us the road map 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

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 servil, bland AI, grapile, reductive 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 scaling and that's where I think we want to be the entire platform for these customers that allows them to do that.

So as like 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 is again what does it

mean to sort of do that in like an AI native way like maybe like give us like 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 and help us

understand the diff.

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 techch companies um in contrast warp um we serve now over a thousand 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 a thousand uh without going into the super locality

municipalities um all 50 states and we have up until recently we had only one one and a half taxperson 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's 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. Because I think it's a very

product. Because 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 it.

For someone starting like an AI native company, um, you know, the advantage you have against incumbents is 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. on on the other hand like incumbents in any space they they build up of 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 new um a new entrant in a

space using sort of like um AI native as the as the angle I think that netn 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 preai sort of the the latest stage SAS where it felt like the the terminal state of software was this like SAS and mega SAS software. It had

gotten quite boring in some ways. I

don't think I would have started a company in that era necessarily in this direction. And in that time I think the

direction. And in that time I think the advantages were really towards like mega distribution salesoriented founders and you kind of see that in the

backgrounds of those companies that got started and I think today and really with AI it really shifted the power of 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 look lots of these channels and partners that

exist 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 entrance figure out how to become the next generation entrench and big software companies. And I think that as

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 chat bots on top of existing architecture and existing customer bases and install bases. And in some ways, I think the

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 from their recent highs and um I I think that to me is underappreciated right now.

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

this idea that um you know you mentioned sort of workday as an example late stage SAS company um it feels like the belief right now is that at least consensus

conventional wisdom uh is a lot of SAS is not defensible against AI disruption that there just won't be any need for it that systems of record will be defensible that those have real moes

against sort of um agents doing all the work and this AI future I think warp would be classified as a system of record um maybe just explain to us like what does that actually mean when people

talk about a system of record what is that and why is that sort of theoretically defensible against um sort of future where AI agents are are

removing the need for software 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 fraction.

Share truth.

Share 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 processes. So your CRM is is in many ways like I think uh we we actually 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 got to get on Salesforce classic

because we are missing out all these 20 integrations to all these tools right 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 found that like actually Salesforce CRM is actually quite

defensible but in general I I think what's interesting is um we 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.

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

different? Why won't it be sort of you know the essentially just a database with some semantic mapping on top of it.

The the reason I think 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 not not in a good sense where like yes they're click house for example is a lovely database company 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 sure 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 mind with the agents is where I think the interesting

stuff is going to happen and what will be defensible because I I there's an interesting chart I think Mintify for example was talking about this cool company that most of the API docs are

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

Cloudflare I think 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 the first time

and I think we've yet to see true enterprises and true enterprise software become like that flipping 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 day by day one from day one built with intelligence in mind.

Make something agents want. It's like

basically the the mantra around here.

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 C that made the move with like such high conviction and

um and make such a big bet on you?

Well, ultimately you'd have to ask battery, but I think the from where I see it um we ended up getting preempted.

This was just uh less than 10 months after series A. Um I think 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 number of

AI native challenggers um some are growing really quickly if you look at something like ITSM um servo who's now a customer um is doing really well and growing quickly and couple of other

companies then ERP there's related campfire which is a YC company 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 in some awesome customers. But I think when it

awesome customers. But I think when it comes to investors, I do think these tend there's a little bit of that narrative flip that 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 start feeling that a little bit more. My that's my guess of why it happened very quickly.

You 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 war?

So now that we have 60 million um and more actually from most of the funds we had from our series A as well, we've 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 401ks life insurance etc 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

ourselves. We have built out global contractor products um uh offer letters traditional HIS 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 softwares and 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 the most capable agent harness

that can reliably and with tr us 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

any sort of specific things in within all of that that you're you're personally really excited about 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

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 registrations 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 pro add them to this uh

healthcare plan create add them to this sickle 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

forth. Right? So all of that you can just script that be a natural language not some like really complicated codel 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

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.

Great. Okay. Well Aish 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. It's been

great to have you as our partner and YC on this journey. So, um, thank you so much for having me.