Adarsh Hiremath @ Mercor: The Fastest Growing Startup in Silicon Valley | E1261

the round is 100 million and the price was it was at 2 billion yeah I think we'll live in a world with many many models with different use cases we're already seeing this with a lot of application layer companies where they

all have these specialized use cases for how they want to leverage the models I think being a recruiter is the highest Prestige position in any company the recruiter is the one who controls the talent inflows and outflows of every

company and pretty much you can gather all you need to know about a company from seeing the talent inflows and outflows the businesses that succeed in a world where software costs approach zero will be built on network effects

ready to [Music] go I am so excited for this dude

listen I've heard so many good things from Pat Grady from Nico from anneal from Scott sandel even so thank you so much for joining me thank you for uh having me really really a big fan of the

pod that is very very kind of you do but I did my stalking beforehand and everyone told me about your Mastery of debating you and your co-founder sria you would debate Champions how did

debate prepare you for founding a company let's start there yeah well I mean one thing about Brendan surria and I is that we actually go quite a ways back so I actually first met suria when

I was 10 years old um and the reason we got along so well is because we were pretty much the only Elementary schoolers who wanted to compete in high school debate so at the time we did

Lincoln Douglas debate which is sort of like a one-on-one debate format sir and I actually even debated each other uh a couple times and then we ended up at the same high school bman which is also where I met Brendan um and then all

three of us were on the debate team together Sur and I decided to do policy so we ended up being debate Partners together and then going on and competing in all these natur National tournaments

um but but debate is a lot like founding uh in a lot of ways right like I like to think of my debate partnership with suria as sort of my first startup just because we had 50/50 equity in each other's success if one of us were to

mess up it would tank the odds for both of us uh there's like this constant feedback loop after every debate round about whether you won or lost picking the right debate partner is like the most important decision you can make in

policy debate and similarly picking the right founding team is the most important decision you can make while starting a company so there's that parallel and then there's just the immense amount of ownership right you know we both have a stake in each

other's uccess at the time you were like 1819 and you can correct me if I'm wrong there but you Brandon Sera get interested in labor markets how does

that happen actually so Brendan Siri and I started working together um without any business ambition necessarily we we just started a a Dev shop together so we were like cool you know let's learn how

to build software really really quickly let's go to these startups let's figure out things they want built let's build it together and what we ended up doing is recruiting these really really EX exceptional folks from India to help us

out with our Dev shop and then very very quickly we realized you know the software was one thing but we had found some really really exceptional people and it was more about the people than

the software so then we were like okay we found these people in a completely manual way can can we automate this uh and that's how the automated candidate

side of the platform was born and then very quickly we realized Brendan sir and I couldn't scale well by doing sales manually so then we had to automate the other side of the platform too the the company facing platform and that's how

the marketplace was born okay so the marketplace is born we've automated both sides of the platform how exciting except you're at Harvard at the time I

think um and now you have this very vibrant and working platform take me to that moment and the decision between whether you drop out or whether you stick to the traditional course well

it's funny that you say I was at Harvard I was definitely there physically not sure about mentally I was pretty much doing everything I could to avoid going to classes and I actually have a pretty uh funny story about this so you know

Brendan would visit me pretty frequently at Harvard and my roommate at the time Artemis had this like really really weird sleep schedule he would just like go to the engineering building and pretty much be nocturnal so the routine

that we would typically follow is Brendan would visit me he would just crash on artemis' bed because he'd be in the engineering building just working on problem sets and then he'd come back wake Brendan up and then we'd get to work together and then he would go to

sleep during the day uh and then you know fast forward today Artemis has joined the the mord team so yeah it really was the typical dorm room story

so when you're then deciding am I actually going to leave Harvard it's one thing to say it's another thing to do can you just take me to that moment at the time it wasn't obvious at all that

we should drop out and I really really sympathize with my parents at the time for you know not approving because here I was we hadn't raised our seed round we hadn't raised the series A there is no teal Fellowship there was

one side of the marketplace that had a little bit of Revenue and I was telling them that I wanted to abandon my degree program um so it wasn't an obvious decision at all but I think you know like most of these decisions you just

make them completely emotionally and I just knew I wanted to work with my best friends for a lot of students who are wanting to start a business who have a business already how do you advise them

on whether to drop out or whether to stick to the traditional path often times it's it's an emotional decision like you can try to rationalize dropping out or uh starting a company or try to

figure out the exact you know set of prerequisits that you have to do but like for me for example the moment I knew that I wanted to drop out was was actually back when we had an office in

Paula walto and the office had exactly three desks one for Brendan one for sua one for me and I was like sua man should we should we drop out and then he just

looked at me and he was just like dude how hard could this be wasn't a logical argument at all but in that moment I was just like let's do this let's let's drop out of school where was the business at

at this point just to frame it no seed round a little bit of Revenue no series a no teal Fellowship nothing we were just three friends uh working in a small

office in PA walto with with our amazing team in India take me to the seed round dude like how did it go do you remember getting the term sheet just take me to that cuz you're 18 19 at the time yeah I

think we were we were 19 at the time so that was that was just surreal so what ended up happening is initially we thought we wanted to base the company in New York so I'll take credit for making

the the wrong uh call there I very very quickly realized that it was the wrong decision but what ended up happening is we had moved to New York before raising

the seed round and for me actually the more surreal moment wasn't actually when the money hit for for the seed round it was actually when

we changed our uh like salaries in Gusto to $500 a month I felt like we made it at that time I was like amazing you know we just moved to New York we changed our salaries to $500 a month and then

afterwards we closed our seed round um and then when the money was wired we were just looking at the account like you know dude I'm just fascinated how was that process like did you pitch many Venture investors did the round come

quickly how much did you raise just take me do it it's a special moment so so we raised over 3 million and it came very very quickly so uh we General Catalyst

uh led the round and really really enjoy working with Max and Nico so dude you are one of the fastest scaling companies in Silicon Valley in the US in startups

in general um unbelievable it was 50 million I think of AR in November I quoted it wrongly it's you may be able to correct me but it's much more now um

with 30 people at the time of the 50 million and I've heard a little rumor on the Grapevine that you do 996 so 9:00

a.m. to 9:00 p.m. 6 days a week can you

a.m. to 9:00 p.m. 6 days a week can you unpack if that's true why you do it and how that works in

reality yeah yeah it it's really funny um a lot of people ask me this question about the 996 thing the only reason we actually just floated those numbers out is because we didn't want our team

working on Sundays um so I like to think of the 996 stuff as more of like a side effect than an objective we've just really really carefully selected for working with people who care deeply

about the mission and the side effect about that is they don't want to wait until Monday to to move the company forward um so people really do it just because they enjoy being each other's presence they enjoy what they're working

on do you worry about creating a hustle culture with 996 I think to some extent this is not something unique to merour it's like all

this successful companies have had pretty intense cultures historically and it's just a function of uh the startup right you got to work harder than everyone else obviously a sustainable

way uh to succeed but like the one thing I will say about that is that momentum is very very energizing and I think everyone on the team feels energized everyone I spoke to

also said that you're sucking up the most ambitious young hungry talent and that it used to go to say a scale or a stripe of old and now it goes to you what do you think you've done to create

a brand where the youngest most ambitious young Talent wants to go to you now I think when we select for people to to work at merour one

realization that we've come to is that you can teach people a lot of things whether it be you know technically or going to Market or whatever but the one thing that you can't quite teach people

is to care and that's one thing that we index on pretty heavily in our hiring process and something that we really look for I had that you've been growing 50% month on month continuously for for

quite a while now that growth to keep up is is insane how does that feel internally and what's the first thing or two to break the way I like to think

about that level of growth is it's basically a Perpetual stress test on the business things are are constantly breaking whether it be process or you know you might need to hire people to

fill in gaps quicker than you might ordinarily need to do or or whatever but I think the main thing is everyone in the company needs to keep outgrowing themselves right redefining what's possible for for them taking on new

roles what does no one tell you about scaling that you wish they had told you scaling culture is harder than scaling software when you're adding people to the team very very quickly there there's

this Dynamic that the culture that you create with the first you know 20 people is in some ways the strongest the culture is ever going to be and ensuring

that that culture stays strong as the grows uh does new things and new people enter the company is really really challenging but in some ways the most important part of building a legendary

company we mentioned scale earlier one of your investors said to me that you are mostly doing data labeling for foundation models do you think that's fair and is that a niche market or a

wedge into a much larger market in your mind yeah so actually our insight about the market is that human data and talent assessment have actually become the same

thing right where you know I can take you back 5 years where when we think of this data labeling or human data stuff it's essentially a crowdsourcing problem right let's say weo wants a bunch of

their images labeled you get a bunch of people across the world to draw boxes around stop signs to make the model better at classifying stop signs but fast fored today and the nature of human

data work has changed a lot now it's GPT 40 or whatever model is not good in a particular domain so we actually need an expert to make the model better in that

domain and figuring out who that expert should be is 100% a talent assessment problem uh and is a perfect application of the platform with a lot of the the

labs that we work with uh we're able to figure out who are the exceptional people in very very specific domains um and have those people work with the with the labs and the interesting thing about

this is that it's essentially a force function on our long-term objectives right when you think about maror building This Global unified labor

market what do we need to make this happen we need tons of smart people on the platform and we need the ability to predict job performance and figure out what those people should be doing uh

which happens to be the exact set of problems that a lot of the AI labs are having when we think about like the AI Labs today I heard through the gra line that as you mentioned that you work with

some of the top AI Labs m mer experts how how does that fit into these Labs what does that partnership look like just help me understand this it it looks

exactly the same as placing someone to work at any company right so just like maror might work with startups making their first hires or companies hiring in

a more traditional full-time capacity it's the exact same thing for uh a lot of the large AI Labs they'll hire people through the more core platform uh to essentially help with post ring models

when you look at today what is the like satisfaction on a higher basis is 90% of higher successful as 60% what are the metrics that you place and what is the One Core metric that you use for the

success of the business customers keep growing their relationships with us so net retention is over 100% by by a large margin so you know as long as they keep

expanding it means that we're doing a good job at finding the right people when you get hires wrong are there commonalities in why you get I'm wrong it's all dependent on the role right and

what you're what you're looking for so at the end of the day there might be commonalities uh but it's all very very role dependent there are many examples that I can think of right but it different companies value different things right uh and depending on what

they value we can correct uh the the talent prediction what role are you best at what role are you worst at it's an interesting question because we place

all kinds of talent at companies right everything from software Engineers to lawyers to doctors to financial analysts

to Consultants so like a huge part of the moror platform is actually not like building specifically for any of these roles but instead Building Technology that generalizes really really well

right you know one example is the AI interviewer we we've built it in such a way that it can immediately pre-process someone's background and then administer a custom interview to a person

regardless uh of what role they're they're trying to take on um in a completely automated way you can literally spin up this interview in under 10 seconds so you know for example for this you know podcast right you you

must have spent like a decent amount of time doing research but imagine like you can just have an agent pull in all the information on someone's profile and put together what would be the Superhuman

interview or the superum podcast that stuff is possible now uh and it's possible for pretty much all roles in terms of an infrastructure basis what

models are we sitting on top of today so it's interesting because the model landscape is just changing so so quickly but we we leverage a variety of models

and have been particularly uh thrilled with the open AI models okay so have we always been on open AI predominantly we've always used open AI in some

capacity if improved in terms of like any aspect of the model A what would make the biggest Improvement on the business and the product today for you with I think a concrete example would be the

AI interviewer we've built the product in such a way that whenever the models improve uh the experience for applicants on our platform also improves uh pretty

significantly and you know in general this is something that's been you know on our mind right there's like this huge wave of models getting better and better and better and can we ride that wave to make our product better and better and

better so to summarize well we leverage llms and all these models uh throughout our product um I think the whole product gets better as the models get better and one a specific example is the

interviewer what do you think will be the next generation of models in terms of what they look like first before we get to training data the whole Market is is Shifting to reinforcement learning

right you're you're already seeing this with 01 03 the Deep seek models and as a result I think we're going to see really

really powerful models in specific domains that can reason extremely well and I think that'll be really really exciting and unlock just a huge number

uh of use cases across a variety of different Industries and domains do we live in a world of many many specialized models but very fragmented or do we live

in a world of monoliths with one or two very horizontal platforms I think we'll live in a world with many many models with different use cases right we're already seeing this with a lot of

application layer companies right where they all have these specialized use cases for how they want to leverage the models right for us it's hiring and beating the expert hiring manager um by

a large margin for another company it might be Financial an analysis in a specific domain so across each of these use cases I think these companies will

need to make their models better for for their own purposes how fair do you think the analogy is that the model landscape will be very much like the cloud

landscape in terms of you know bluntly three or four Juggernauts and it being very hard to switch out of do you agree that it's hard to switch out of them or do you think given the model transience

it's actually much easier and much less defensible in that respect there will only be a couple of companies that are able to build these Foundation models that everyone builds off of I think

opening eyes is a great example of one of those companies and I think you know that analogy roughly holds I don't anticipate there being 20 companies trading Foundation models that can all

be be leveraged in the same way someone might leverage uh open AI for example in terms of like the posttraining data side I'd just love to hear your thoughts on

how much will be human data versus how much will be synthetic data moving forward I think a lot of it will be human data going forward and I think a

great example of this is evals right evals REM models definitionally have to be outside of model capability right in order to see whether model is doing well at a particular task you need to have an

eval set created by humans that is better than the model at that particular task and humans are going to play a huge role in that for example and I think there are whole set of other use cases

whether it be sft rhf you know RL environments like how the models of Tomorrow are being trained that all require these expert humans uh to essentially teach the model how to get

better to what extent would you say that data is the bottleneck that prevents model Improvement more than compute or algorithms data is the bottleneck I think that would be an accurate

statement why then does so many people tell me including Jonathan at Gro that actually synthetic data is often more high quality it doesn't involve the

drags of the internet like RIT um in a lot of cases being included and actually you'll see this exponential increase in model performance due to mostly using

highquality synthetic data not lowquality human data why is that wrong so the first thing is that it's not Zero Sum right even in a world where

human data is super important for the next generation of models it doesn't mean that synthetic data won't also be important so synthetic data will certainly be a part of the equation but in a lot of ways the bottleneck to

unlocking and unleashing the next level of intelligence will be expert humans which brings me back to the question and phrase that you used lowquality human data lowquality human data

certainly won't push the models to be better at anything uh high quality human data will and again that's a talent assessment problem the biggest lever on data quality for creating these post

training sets for example is finding the right people um which again is is really really hard to do in terms of like Computing algorithms how do you think about where we're at today in terms of

them being a bottleneck we mentioned data is a bottleneck all compute an algorithms too how do you think about that all of them are pieces of the same

puzzle so going forward I do think you know compute data algorithms will all play a part of of kind of like the

equation and moving AI forward and Unlocking The Next Level uh of intelligence but the era that we're entering uh requires really really

expert humans to to make models better uh very very specific use cases how long will that be the case for for a very very long time why I thought this was

what we were getting rid of there is this huge longtail of tasks that models can't do I think when people I'm so s to be like what just help me understand

you're teaching me yeah yeah yeah it could be you know well maybe we could take a step back you know and just talk about you know labor in general if we

reach the point a couple hundred years from now where the models are able to do every single job and humans no longer have any work to do Society is going to

look really really different right we're all going to be living on a Ubi we're all going to be playing video games all day whatever it may be but until that point comes there's going to be a whole

set of tasks that the models cannot do whether they be specific you know economically valuable tasks like maybe the job that a consultant could do or

maybe a specific category of engineering or even like more Niche things right maybe it's making the model better at like some specific hobby uh for example

and we're always going to need to fill in the gaps particularly in that longtail and Harry the other thing I'll say is I think people are really really in this mindset of like this like unid

directional relationship between humans and AI right where I can't do something I give it to the AI the AI takes it to completion but I think the more

realistic breakdown is the AI for a specific use case might be able to get us 60 70 80% of the way there but for

that remaining 40 30 20% you're going to need a human to be able to to take you all the way there and the reality of the situation is finding that human will

become harder uh and more valuable to do if you get further andur further up the spectrum of we're able to 70 75 80 85 90

do you not need fewer and fewer humans because the frequency is much less as you move closer and closer to perfection that's a great question and I think that begs the question of what do labor

markets look like uh later on I think the key thing is that the market will move uh towards specialty and

sophistication meaning the types of work that we see 50 years from now will be more specialized and often times will require people with kind of like a

higher level of sophistication in that specific thing when you sell to clients what is the moment where they're like wow we've got to use

my when we're able to find exceptional people uh at the cost of software hundreds of times over but when you're in that sales cycle with them today when

are they going yeah we've got to sign up is it when they see the AI interviewer is it when you show them the price is it when they meet a candidate like what when is that wow moment for them it's

usually when the first couple candidates start working with them how do they tend to sign up is it like hey what what is that buying process they buy one at a time is it on a per Talent basis is it

on a timeline basis how does how does a deal with MCO work one interesting thing about maror is we don't have a sales team there's not a single person who

works uh on sales at maror uh outside of you know the founders and these days what we're seeing is mostly customer inbound so folks have heard great things

about maror uh from other people who have hired through maror and then reach out to us and then we go from there so right now it's more of a a bandwidth thing than than any like tactical or

coordinated sales motion I would say what percentage of highs is end to endend done by software versus has human in the loop so on our end the entire

process is automated so this is everything from a candidate hearing about maror and going onto the maror platform via job listing us pulling in

their resume their salary expectations and whatnot administering a personalized interview based on both their background and the role allowing them to get paid

for their work that entire process is automated what does a take look like on a per candidate basis it all comes back

uh to Quality so you know I briefly talked about Uber and just going back to that example right when I get into an Uber there isn't that much of a

difference between the 4.8 star driver and the 4.9 star driver because the unit of work is not exponential but with something like merour there's a huge

difference between the top .1% and then the 80th per person so

.1% and then the 80th per person so usually for customers it's not a question of price it's a question of

quality and if we're able to find those 0.1% people or 1% people reliably at the cost of software uh and Delight our

customers what what we take is often a second thought I'm sorry what is that take then is it like a standardized take is it like on a caseby casee basis what does that look like it's on a case- by

case basis for some customers it can be you know over 30% for some it can be less when you look at Canada completion rates how much of that is India versus rest of world today I know you

specialize in finding amazing talent in India specifically so the reason we started with India is because you know our parents uh immigrated from from India Syria and I so uh they went to

these amazing schools so we like started these recruiting campaigns from those schools specifically and actually like one of the things that got us really really excited about you know labor market in general and the inefficiencies

associated with it was just because like one of the best Engineers I've ever worked with on our team we found through a Facebook ad and I manually interviewed

him and actually he didn't pass the interview uh but the reason that we ended up hiring him is he sent me a really really long message about what exactly he got wrong in the interview

and how to correct it and and I just felt like we got to work with him it was sort of that that prompted us to start an but if you fast forward to today

actually the number one place that you know workers on the mer platform who you know have jobs through through us um are from is actually the United States what

percent wise is like 60% us style it's it's it's it's high up there yeah and client wise all us too mostly us yeah a lot of young exceptional

people are being told today that they shouldn't maybe study CS anymore because is actually CS is becoming so automated uh 41% of code is now written by AI in 5

years time that'll be extortionately higher do you agree with that advice and how do you think about whether or not young people should learn programming today my take is that programming is

actually more important today and it's just going to happen at a different level of abstraction one could argue that the leap from assembly to python

was actually maybe even a bigger leap than the leap from python to natural language So my answer there is that the way we Define programming will look very

very different it may be a person who you know has like average skills by today's standards in in computer science who's orchestrating thousands of superhuman coding agents to achieve more

than we thought uh was even possible um but that skill set which you know we can Define as programming at a different level of abstraction programming in English is going to be super important

can I ask how has the way that you program changed over the last two years yeah I I definitely use a lot of the the AI tools um they've gotten really really

good uh a great example what do you use and how has it changed how you work a great example is cursor uh a lot of members of our team use use cursor and

love it uh I'm one of them how's it changed how you work it makes doing you know things that would take a lot of time just so simple and elegant right a great example is testing with a couple

of prompts you can just generate a more thorough test Suite than than you know anyone could have imagined uh for your application for example um and that just wasn't possible before or maybe it's

like bringing the same consistency from like one part of the code base and refactoring it for another part of the code base you can basically snap your fingers in cursor and it'll get done

today which is absolutely insane to think about um and I think like the implication for software is that software is going to get commoditized uh very very quickly as these coding agents

get really really good what does a world look like where software as commoditized what does that mean it means that people will be able to build applications much

faster than was historically possible and it also means that the businesses that succeed in a world where software costs approach zero will be built on

network effects the company that don't could even give away their entire code base and it still be alive right uh the marketplaces and companies like meta you

know and Airbnb that have built these really really strong Network effects will be the ones that thrive so do you believe if you agree with people who say like oh SAS is dead because companies

will just build their own software or do you think differently I think what we consider SAS will change right in the sense that the next era of SAS will be replacing

entire Services right you know whether it's the end to-end process of a recruiting agency like like maror uh or another you

know different service or that that is incredibly manual uh and Incredibly repeatable you said about Network effects there if I were to push you to

the strongest Network effect that you have within mcco today what do you think it is break it down as in two categories right so one is the network effect of a

Marketplace that you might see in a labor Marketplace like uber uh or a Marketplace like Airbnb where every additional company that hire through mer cor strengthens the marketplace and

every additional candidate on moror strengthens the marketplace as well because there's like a higher pool of really really exceptional people to choose from and the second Network

effect is uh or or like data flywheel is around this job prediction piece where we're able to see who's performing well on jobs and the specific reasons

why they're performing well on jobs H and use that that endtoend data uh on people's outcomes to to make it really really easy to surface uh the

person that might be the best for for a given role even if they themselves don't know it how do you think about building like stickiness and switching

cost and making sure that 50 million is really sustainable it all starts with quality and I think a lot of the greatest

products or companies of Our Generation have been usage based right I think stripe is a is a great example of this and the reason that that revenue is

really really sticky uh is because you're able to create these like six-star experiences uh for customers and candidates and I think that's one thing that has resulted in our very very quick Revenue ramp right when you think

about the product today what would you most like to change that Brandon and Sera would most not let you change maybe running our entire you know internal

hiring process for maror in a completely automated way meaning Brendan Sur and I don't even talk to someone when they come into the office H and then we we

walk in the conference room to meet them the first time and we're just like wow this person is awesome and like we couldn't have found this person even if we spent all day every day trying to

find this person and we're we're getting there and that's just super super exciting for us how do you think about the future of remote and remote versus

in person I don't think you can do 996 and do it effectively in as your own person I think that motivation that intensity you feel in the same room yep

and that's exactly why we do you know in person in San Francisco Brandon Siri and I all get super energized by being around people people and I think a lot

of our best ideas for maror have uh come when we weren't even in a meeting right we were just sitting around chilling uh discussing things and then you have that aha moment and I think there's something

really really special for in person what was the worst product decision you made at one point uh Brendan Siri and I all thought that chat was the future of all

UI at that time a while back so one of the iterations of the mer product was just built around a chat interface like

there was pretty much no other way to hire people unless you use the record chatbot because we were so bullish on on chat um I think we've come around to

that we now like mix chat with with other things uh were applicable or leverage llms in other ways but for a while we thought like the concept of a web app tomorrow would be dead and and the way you would interface with all web

apps would just exclusively with chat so it wouldn't even be you know you clicking a button to hire someone it would be you telling the chat bot to hire the person I think you know it's possible down the line but we we may

have mistimed a little bit in terms of funding dude you you've got some of the best on your cap table you mentioned GC at the start told to me you've rais quite a few rounds in quite quick

succession how did you think about that and do you agree when the money's on the table to take it an interesting Dynamic about of all of our you know fundraising rounds um

it's just that like we didn't intend on on doing the fundraising at the time and it sort of just came to us you know going back to the example uh about

Benchmark um someone introduced Brendan to Victor Brendan said we were heads down uh and then Victor you know convinced Brendan to to have a

conversation with him and then the rest was history from there can was it like how did that process go down so Brandon meets Victor and then you guys meet Victor and you have a chat how does that

go down Brendan had the initial chat with with Victor um and then you know afterwards Brendan was like okay gonna gonna get back to work uh and then you

know afterwards Victor asked Brendan if he'd ever been on a helicopter and Brendan said no before you knew it Brendan was on a

helicopter with uh Peter Fenton from from Benchmark and we knew that they were the firm that we wanted to work with very very quickly and so Brandon comes back and goes hey guys they took

me on a helicopter let's do it had a couple more conversations with with Victor and The Benchmark team uh and it was clear that they were they

were the best so we we wanted to be in business with them and and work with them and they've just been phenomenal then how many months later is the next

round the next round was I think about over about 6 months later 6 months later you don't need the money at that point talk to me about that round how did you

think about taking the money then it's interesting because we we again weren't focused on on fundraising right uh we we had built a a business

that was doing a lot in Revenue um you know we're paying out tens of millions to how much is it doing in Revenue at this point it was doing you know eight figures in in Revenue right and and we

were we were just like okay let's let's be heads down um but just like we we felt with Benchmark we we wanted to be you know in business with snep and

felicas and and the amazing team there um which made it a no-brainer do you enjoy fundraising not really not really

yeah do you have a board adash we do it's it's you know Brendan Siri and I and and Benchmark uh on the board is that it that's it

yeah and and yeah we don't enjoy fundraising I think the thing that we really really enjoy is moving the business forward that that's always the thing that that Founders enjoy the most

so we've been you know laser focused on that and sometimes it just makes sense to do around you said about eight figures in Revenue there when I think you raised that one of the rounds were

you aware of how fast the revenue scaling was like were you guys looking at each other going this is this is unbelievable

we definitely had that moment and and at the time we raised that round we didn't realize how much the growth was

going to accelerate right or or we knew of it we were confident in it but just the fact that it even exceeded our expectations uh you know wrapping up q1 of of this year uh is something that

we're we all you know really really excited by and told to me about this new fundraise was this new fundraise the felicis round yep so the new fundraise

was was led by by Fel with some you know other amazing investors including GC Benchmark and others participating as well and how much was this round the

round was 100 million 100 million and the price was it was at 2 billion yeah what a phenomenal round dude like no really like there's brilliant round in terms of dilution you

dilute 5% you get 100 million on the balance sheet phenomenal round for a company to do thank you yeah we're we're really

really excited to be in partnership with snep and the felisa's team they're they're amazing do you need the money like what are you g to spend 100 million

on I think the just I mean that with so much respect and I I like you so muchos I'm just like you guys make a load of money you you raised not long ago like

what are you going to do with 100 million I think it gets really dangerous when people raise the money and then think that they just have to spend it immediately because they raised the

money so our goal isn't to deploy $100 million tomorrow um but Harry I think the thing about our business is that labor aggregation and building this

unified labor market it's going to take a long time we just want to make sure that we have a balance sheet that uh is kind of commensurate with that long-term goal listen I want to do a quick fly around so I say a short statement you

give me your immediate thoughts does that sound okay let's do it dude what do you believe that most around you dis believe I think being a recruiter is the

highest Prestige position in any company because you know the recruiter is the one who controls the talent inflows and outflows of every company and pretty

much you can gather all you need to know about a company from seeing the talent inflows and outflows so I I think it's uh the recruiting function of a company I is the most underrated and undervalued

part of the reason I started maror does the whole we should do more with less efficiency on a perum basis not go against mcco and the importance of

recruiters actually it it goes with it right because efficiency is only possible if you find the right person and solving that matching problem and finding the right person is really

really hard especially with manual processes that don't scale who do you think is the best person in the world at what you do and what have you learn from them it's interesting I've had this conversation with uh some members of the

mord team before I think one thing that we like to joke about is that company exacts are a lot like athletes in a lot of ways where there's

like this drive and desire to to win um you know maybe you know I I had dreams of being a basketball player a while ago definitely not what I do today but you know one person who I think really embodies that winning mentality is

LeBron uh and I like him a lot so if exact like athletes how do you treat yourself as an athlete I think there's an element of pushing yourself to win and focus on the right things and

getting better every day uh and that's just something that I think about right how can I be the best version of myself tomorrow and be an even better version the next day and continue that and have

that compound for 10 20 years what have you changed your mind on in the last 12 months I think part of it is the you know the SAS uh answer I gave you a little bit earlier that just that over

time it's become pretty obvious to me that the next generation of sass will replace entire Services end to end and I think that realization has sort of been

part of the reason that we've you know built maror in this way what was one thing that you're doing to say that you should stop I have to be honest with you I think it's probably my lime ride to

the office when I'm running late for morning standup like you know we start at 9:00 a.m. every day and sometimes it's like I'm leaving my apartment at 8:55 and I'll just like take a lime and

go straight down the hills of San Francisco in the most unsafe way possible uh so I should probably stop that what do you know now that you wish you'd known when you started MC I would

say just how hard it would be to to build a business like this I told you you know back when we decided to start marcore it was like a complete you know emotional decision where Sur just looked

at me and said hey man how hard could this be you know Brendan came in with his optimism and we just did it and I'm thankful for that but I I didn't really have grasp my mind around how hard

building a business like this would be you can have anyone on your board who do you have I would have to pick Sam Alman you can ask Sam Alman any question what

do you ask Sam I would probably ask him more about what AGI looks like what would you want his answer to be I think it more comes from a place of curiosity

rather than I know Sam Sam would turn it back on you and go why didn't you tell me first what do you think AGI will be when we achieve AGI or or sort of like

think about AGI it will certainly involve you know doing more economically valuable work right so when more and more and more of economically valuable work has been automated to some extent

you know research has been automated to some extent I would broadly put that in the bucket of AI it's 2035 okay MH final one where is mcco

then paint that picture for me of how big you are how many people you've placed where is mcco have to work backwards um a

little bit right so how many job Seekers are there right you know roughly put it in couple billions um how many jobs do does each people you know does each

person take on right people change their roles so you know maybe we factor out all the jobs maror creates for AI agents and like roughly focus on just the jobs

for people create couple dozen jobs for each person marus created 100 billion jobs and uh has built the the unified uh

labor Marketplace meaning that anytime a company wants to hire a person for a specific job or task they do it through maror and anytime a candidate wants to

consider a company for a specific job or task they do it through marcor and marcor is able to solve the matching problem across every role across every

company in in a seamless way would you love for mcco to be a public company one day one day listen adash I've

peppered you with questions thank you so much for putting up with my uh very Wayward approach to a schedule but you've been fantastic so thank you man thank you for having me it was really fun