Why experts writing AI evals is creating the fastest-growing companies in history | Brendan Foody

The wealthiest companies in the world are willing to spend whatever it takes to improve

model capabilities. We're entering the era of evals. We started working with all of the

top AI labs. What the labs need is a labor marketplace. They actually need extraordinary

professionals that can measure model capabilities. You found this pocket maybe the biggest business opportunity

in history. We grew from one to 400 million in revenue run rate in 16

months. Fastest ascent in history. Why is this so valuable? The market is

bound by the amount of things where humans can do something that models can't. The

lab's primary bottleneck to improve models is how they can effectively have some

way of measuring what success looks like for the model. There's this tweet that you

retweeted. If you really think about it, we were put on Earth to create reinforcement

learning training data for labs. It's highly likely that the entire economy will become an

RL environment machine, building out all of these worlds and contexts. And I think the

narrative in AI over the last three years has almost entirely been one of job

displacement, but very few companies and people have talked about this new category of jobs

that's being created. I talked to a lot of people about what should I be

studying? Where should I be getting better? How can they leverage this technology to do

so much more? We'll give people interviews where we say use whatever tools are available

to build a website and let's see what product you're able to build in an

hour. Today, my guest is Brendan Foodie, CEO and co-founder of Mercore.

Mercore is the fastest growing company in history to go from one to $500

million in revenue. They did this in 17 months, less than a year and a

half. Brendan is also the youngest unicorn founder ever. They just raised $100 million at

$2 billion valuation. Mercore, if you haven't heard of them, helps AI labs and AI

companies hire experts to help them train their models using AI. They've never had a

customer churn, their net retention is over 1600%, and they're on a nine-figure revenue run

rate. In our conversation, we talk about the increasing value and importance of evals, the

landscape of AI training companies like Mercore, and why they've become so important and valuable,

how Brennan discovered this opportunity, his insights on what product market fit looks like, the

core tenets he's instilled within his organization that have allowed him to build the fastest

growing company in history, what people writing evals for labs are actually doing day to

day, which skills and jobs are going to last the longest with the rise of

AI, why he doesn't think we'll see AGI or super intelligence anytime soon, and so

much more. This episode is incredible. You need to hear this. If you enjoy this

podcast, don't forget to subscribe and follow it in your favorite podcasting app or YouTube.

It helps tremendously. Also, if you become an annual subscriber of my newsletter, you get

15 incredible products for free for one year, including Lovable, Replit,

Bolt, Innate and Linear, Superhuman, Descript, Whisperflow, Gamma, Perplexity, Warp, Granola, Magic Patterns, Raycast, Chappier

D, and Mobbin. Check it out at Lenny's Newsletter.com and click Product Pass. With that,

I bring you Brendan Foody. This episode is brought to you by WorkOS. If you're

building a SaaS app, at some point your customers will start asking for enterprise features

like SAML authentication and SKIM provisioning. That's where WorkOS comes in. making it

fast and painless to add enterprise features to your app. Their APIs are easy to

understand so that you can ship quickly and get back to building other features. Today,

hundreds of companies are already powered by WorkOS, including ones you probably know, like Vercel,

Webflow, and Loom. WorkOS also recently acquired Warrant, the Fine Grain

Authorization Service. Warren's product is based on a groundbreaking authorization system called

Zanzibar, which was originally designed for Google to power Google Docs and YouTube. This enables

fast authorization checks at enormous scale while maintaining a flexible model that can be adapted

to even the most complex use cases. If you're currently looking to build role-based access

control or other enterprise features like single sign-on, SCIM, or user management,

You

fell in love with building products for a reason, but sometimes the day-to-day reality is

a little different than you imagined. Instead of dreaming up big ideas, talking to customers,

and crafting a strategy, you're drowning in spreadsheets and roadmap updates, and you're spending your

days basically putting out fires. A better way is possible. Introducing Jira Product

Discovery, the new prioritization and roadmapping tool built for product teams by Atlassian. With

Jira Product Discovery, you can gather all your product ideas and insights in one place

and prioritize confidently, finally replacing those endless spreadsheets. Create and share custom product

roadmaps with any stakeholder in seconds. And it's all built on Jira, where your engineering

team is already working, so true collaboration is finally possible. Great products are built by

great teams, not just engineers. Sales, support, leadership, even Greg from

finance. Anyone that you want can contribute ideas, feedback, and insights in Jira product discovery

for free. No catch. And it's only $10 a month for you. Say goodbye to

your spreadsheets and the never ending alignment efforts. The old way of doing product management

is over. Rediscover what's possible with Jira product discovery. Try it for free at

Atlassian.com slash Lenny. That's Atlassian.com slash Lenny.

Brendan, thank you so much for being here. Welcome to the podcast. Thank you so

much for having me, Lenny. I'm a huge fan and so excited to have a

conversation. I'm really excited to have this conversation as well. I'm a huge fan of

yours. I'm excited for more people to learn about you and what you're building. I

wanna start with a tweet that you have pinned at the top of your Twitter

feed right now. And here's the tweet, quote, we are now working with six out

of the magnificent seven, all of the top five AI labs, most of the AI

application layer companies. One trend is common across every customer. We're entering the era

of evals. reason this caught my attention is that's one of the most recurring trends

on this podcast. People talking about the increasing value of learning how to do evals

well and the value of evals for companies. It feels like still people don't know

what the hell this is, what we're talking about, why this is so important. Talk

about just what you think people are still missing, what they need to know, what

this era of evals means. If the model is the product, then the eval is

the product requirement document. And the way that researchers day to day looks is that

they'll run dozens of experiments where they'll make small improvements on an eval set. And

reinforcement learning is becoming so effective that once they have an eval, they can help

climate, right? If you look at just how fast people were able to saturate Olympiad

math once they focused on it, how fast we've been saturating Sweebench once we focus

on it. And so in many ways, the barrier to applying agents, the entire

economy to automate every workflow is how do we measure success? How do we eval

it? And write the PRDs for everything that we want agents to do, which Mercore

is obviously a huge part of doing. So people hearing this are like, okay, okay,

shit, I got to really pay attention to this eval stuff. Any advice about learning

how to do this well? What companies that are doing this well are doing differently?

Like help people get better at this thing. Yeah, I think that... For enterprises

especially, the core way to think about it is how can they build a

test or a systematic way to measure how well AI automates their core value chain?

So if it's an architecture firm that's producing, you know, these like architecture diagrams

of what they provide to their end customer, like how can they effectively measure that?

Right. And each company has its own value chain. or maybe a handful of them

if it's a multi-product company. And just thinking about how they measure that

is the prerequisite to really effectively applying AI throughout their entire business. I saw you

talking about this on the No Priorish podcast with Sarah and Ilad. And I don't

know if it was after this or before this, but Sarah tweeted evals equals your

new marketing. What does that mean? What do you think she's saying there? Yeah, well,

it ties to what I said earlier about how if the model is the product,

evals are the PRD, but also subsequently the sales collateral, right? Because like evals are

what you give to researchers to show them what they should be building and going

on. But they're also the way that you demonstrate the efficacy of capabilities. And historically,

everyone's been pointing to these academic evals of PhD level reasoning with GPQA, humanities last

exam or Olympiad math. But now it's moving towards the capabilities that people practically care

about. of how do we get models to automate the way that we build a

software platform or automate the way that we do an investment banking analysis.

And I think labs will increasingly use, labs as well as application layer companies, will

increasingly use evals to demonstrate the capabilities of their models and their products. Okay.

So let's kind of build on this and zoom out a little bit and talk

about the landscape of the market that you're in. And I was just reflecting on

this as I was preparing for this conversation. If you think about the companies growing

faster than any company's ever grown in history, there's essentially three buckets. There's the foundational

model companies. There's Vibe Coding Apps, Cursor and Lovable and Bolt and Replit and all

these V0. And then there's data labeling data companies like you. So I've had the

CEO of Handshake on the podcast. I have the CEO of Scale coming on. There's

also Surge. There's you guys. Help us just understand the landscape of what this is

all about, because I think people don't really know what the hell is going on

and see all these companies growing like crazy. Yeah, I'll give a little bit of

the origin story and that and how it sort of frames the landscape. Because when

we started the company, I met my co-founders when we were 14 years old. We

started the company together when we were 19. Initially, in January of 2023, initially hiring

people internationally, matching them with our friends and automating all the processes of how we

did that. So similar to how a human would review a resume, conducted an interview

and decided to hire. We automated all of those processes with LLMs, bootstrapped the company

to a million dollar revenue run rate before we dropped out of college. And then

a handful of other things happened, but we met OpenAI and we saw that there

was this enormous transition in the human data market where it was moving

away from this crowdsourcing problem of how do you find low and medium skilled people

that can write barely grammatically correct sentences for early versions of LLMs and moving towards

this sourcing and vetting problem. How do we source and assess the best professionals, the

experienced Thang software engineers, the investment bankers and doctors and lawyers

that can actually help to evaluate and interpret all of the capabilities that

people want models to have. So from there, we started working with all of the

top AI labs. We grew from one to 400 million in revenue run rate in

16 months. And it's been an extraordinary journey and super exciting.

Okay. First of all, that is out of control. I don't know if people have

understood. I think this is the first time you're sharing that number. I know recording

this, you'll have announced it by now, but one to $400 million in revenue

in 16 months. Exactly. So fastest ascent in history,

which is an exciting statistic we're very proud of. Okay. So something big is happening

here. Why is this so valuable? What is going on here? So it's just to

try to summarize what you guys do simply is you help hire people for labs

to help them train their models. And you help them find not just generalist labor,

but experts helping them with very specific gaps in the model's knowledge. Yeah,

precisely. And so it really ties to your first question around the era of evals

that's framing all of this, which is that the lab's primary bottleneck to

being able to improve models is how they can effectively have some way of

measuring what success looks like for the model, both to use it as the eval

for the test that they're measuring their progress against, as well as the verifiers in

an RL environment to then reward the model, improve capabilities, et cetera. And

they need this across every domain for every capability that models don't know how to

use. And the wealthiest companies in the world are willing to spend whatever it takes

to improve model capabilities where Mercore is sitting at the forefront and sort of the

primary bottleneck. Okay, what are these people actually doing? What's an example of a kind

of person that is sought after and then what are they doing like sitting there

at the computer? Effectively, the market is bound by the amount of things where

humans can do something that models can't. So I'll make that very concrete. Say you

have a model that you want to write like a red line for a contract

in the way that a lawyer would. And it makes a handful of mistakes, misses

a bunch of key points in doing so. What you could do is have a

lawyer create a rubric. similar to how a professor might create a rubric to create

a deliverable for what are the things we want the model to be able to

do so it can effectively score that, right? Like, you know, plus however much of

it identifies this or, you know, XYZ key point. And that's really the

foundation to measuring what does progress look like for models? You know, is this model

achieving the capabilities that these professionals want, as well as how do we use this

as training data to reward and to reinforce a lot of the capabilities that

people want models to achieve. Okay, so they're essentially writing evals just to connect it

back to original conversation. Exactly. Well, that's an interesting thing is everyone talks about

RL environment. I feel like the two like hot button things are like RL environments

and evals. But one thing like Andre Carvathies tweeted out about a bunch is there's

not actually a nuance. It's in the data type. It's more just a different semantic

way of describing what it's being used for. But ultimately, it's just some stasis point

for like, how do you measure what good looks like? And you can use that

either as the benchmark to, you know, the sales collateral, as Sarah was saying, to

say, here is why our model is the best model in the world. And here's

the capabilities that we've been working towards. Or you can use it on the post

training side to reward certain model trajectories and achieve those capabilities. Okay,

so say this lawyer, so this person is writing, here's what a great red line

contract looks like, and here's the rubric of what excellent is. And then are they

also providing data, like actual examples of red line documents as a part of that?

They may. So the data landscape historically has included two kinds of data.

The first is supervised fine tuning data, which is input output. When people think about

like fine tuning in the historical sense, that's what it is. The second is RLHF.

where the model will generate a couple of examples, we'll choose, you know, which is

the most popular example. What everyone is generally moving towards is reinforcement

learning from AI feedback instead of human feedback, where you have instead the human

defined some sort of success criteria, some way to measure that. And examples in code,

it could be a unit test, right? We can scalably measure success in other domains.

It could be a rubric. And then you use that to incentivize model capabilities.

And it's far more scalable and data efficient. And so that's why a lot of,

you know, the broader trend in the market across the board is moving towards RLA-IF

to both eval models as well as improved capabilities. I had the one of the

co-founders of Anthropic on he said exactly the same thing. That's what they've done at

Anthropic is move towards AI driven reinforcement learning. So essentially, If I can understand this

correctly, I'm the layperson here trying to understand this on behalf of the audience. So

essentially a lawyer is like, here's what correct looks like for redlining. And then it's

AI is just on its own, almost just like, here's all the, I'm going to

try to get this. I'm going to try to improve on this. And I know

if I'm heading in the right direction based on this eval slash rubric I've been

given. Exactly. Applying all of the criteria of what good looks like, similar to how

the TA might apply the professor's criteria of does the student's response meet this criteria

or this criteria plus however many points, et cetera. Awesome. Okay. Let me shift to

talking about the broader labor market here. So there's kind of two parts to this

question as we talk about this. One is just how long will we need to

do this? Is there a point where we don't need? Like you guys grew so

incredibly fast. Is there a point in like, okay, we don't need humans or... We're

tapped out. So let's start there and then I'll ask a broader question. So the

key question is how long there's going to be things in the economy that humans

can do that AI can't do. And I think there's certainly a bucket of people

that say we're going to have super intelligence within three years and we, you know,

humans won't play a role in the economy. And that's one school of thought. Our

perspective is very different. Our perspective is that these models are extraordinary and automating a

lot of things very quickly, but there's a lot of things that they're horrible at.

Like even still, it can't schedule time on my calendar. It can't draft emails for

me. It can't use basic tools. And we need evals for everything. For everything that

the models can't do, we need evals for the tool use, evals for the long

horizon reasoning. Like imagine in 10 years when we want models to be able to

go out and build a startup for 30 days. Like we need evals for that

to effectively reward it. And I think that that road to improving models

will last for as long as there is anything in the economy that humans can

do, which models can't, and be a huge portion of what the future of work

looks like. And so our mission is creating the future of work. And I think

that this is a really exciting industry in giving us a glimpse into the direction

that everything is headed towards. There's this tweet that you retweeted that I want to

ask you about. If you really think about it, we were put on earth to

create reinforcement learning training data for labs. Yeah. What does that

mean to you? What is this person implying? And it's basically what you're saying is

we're just helping train models. It speaks to conversations I've had with a lot of

researchers and executives at top labs, which is that it's highly likely that the entire

economy will become an aural environment machine, building out all of these worlds

and contexts for us to then have rubrics or other kinds of verifiers.

And that is really exciting in so many ways. Because I think like, let's draw

analog to other revolutions where when we had the industrial revolution, revolution, everyone was freaking

out about losing their jobs. But there was this whole new class of jobs of

how do we build the machines? How do we have knowledge work? How do we

create everything new? And I think that the narrative in AI over the last three

years has almost entirely been one of job displacement, right? Sure, there's like ChatGPT is

growing fast and it's very cool and everyone loves using it. But from an economic

standpoint, people talking a lot about job displacement. But very few companies and people have

talked about this new category of jobs that's being created and what that's gonna mean

and how people can prepare and upskill for that. And I think that the most

exciting thing possible is creating that future of how do humans fit into the economy

and how will that evolve over time. I talk to a lot of people about

just like, what should I be studying? Where should I be getting better? People in

school right now are just like, what is even gonna be valuable in the future?

You're at the center of a lot of just what jobs are most in demand,

what how hiring is evolving. So let me just ask you a very concrete question.

What jobs do you think will remain in the future slash what skills are still

worth investing in for younger people, especially? In terms of jobs, I would respond with

a category of things that have very elastic demand are going to be super exciting

because when we make people 10 times more productive, we'll build 10 times, if not

100 times as much software, as an example. Right. And so I think the product

managers that can now do so much more are going to be extremely well positioned.

And so far as the skills, I think it's people that can leverage AI to

to do whatever their day to day workflows are like. I have had a couple

of conversations with teachers where they get my thoughts on how they should be assessing

their students because, you know, we originally started out curating all of these interviews and

assessments for people and have thought about this immensely. And what we realized is that

you don't want to fight against them using the models, right? It's sort of similar

to like when the calculator came out, you don't want to give people all of

this, this arithmetic homework of like, how do you, you get them to do it

and not use the calculator. You want to tell them, use the tools and let's

see what you can do. And so we'll give people interviews where we say, use

ChatGPT and codecs, use CloudCode, use whatever tool cursor and whatever tools are

available to build a website. and let's see what product you're able to build in

an hour. And so I think that I give that an example in so far

as talent assessment, because I think it pertains also to the skills that people should

be honing in on of how can they leverage this technology to do so much

more in whatever industry or vertical they're operating in. When you talk about elastic being

elastic, is it like generalists being good at just a bunch of different things or

what do you say? What do you mean when you think elastic? So I more

mean, how much capacity for demand there is in that industry. So I'll give a

couple of examples. Like in accounting, I think realistically, we only need so

much accounting in the world, right? Like maybe there's areas where we can do more

and that'll be good, but it doesn't feel like the world needs 100 times more

accounting. On the other hand, in software development, right? Like I think we can ship

100 times more features for our products, move 100 times faster, build so much more,

there's just, it feels like there's unlimited demand for the industry. And I think Marc

and Dresen tweeted about this recently, that software is the most elastic industry of all,

where when we increase productivity, there's so much more that will be built. And it's

definitely characteristic of a lot of other domains as well. And so I would, I

would focus on those domains where if we make everyone 10 times more productive, that'll

increase demand, not reduce it. Okay, so you're in the bucket of learn to code,

still useful as a skill. Take computer science, okay. And so in terms of elastic

categories of jobs, sounds like engineering, product management is in that bucket, great.

A lot of people listening to those are PMs. What else, like design, user research,

I don't know, what else do you feel is in that bucket from what you've

seen? Yeah, I think that there's a lot of things for the whole value chain

of building companies is a lot of these like variable costs. even large portions of

like operations or consulting, right? Like imagine if we could have

10 times as many McKinsey consultants, what would be possible insofar as the research we

could do, the analysis, et cetera. But I think the companies and people that are

gonna succeed are those that lean into this narrative of abundance of how do we

do so much more rather than fighting back against it of how do we try

to stop displacement. So along those lines, I think about your second bucket, which is,

the people that will be most successful. It's not like a specific skill, but it's

being good with AI, using AI to become more, become better at what you're already

doing. This reminds me of Elon's whole thing with Neuralink, which if, I don't know

if this is how he put it, but the way I've always heard it is

he wanted to build Neuralink because in the future, when AGI and super intelligence is

around, we need a way to compete. And the best way to compete is plug

our brains into a super intelligence so we have a chance. And it feels like

that's what AI is like getting good at AI tools is essentially is having the

super, superpower. Figuring out how to leverage them and incorporate it will definitely be

of paramount importance. Yeah, it just comes back to this almost cliche quote now. It's

AI won't replace you. People that are really good with AI will replace you. I

think it's totally spot on. And I've definitely seen this at the enterprise level as

well, where there are certain enterprises we talk to that are almost like fearful, not

wanting to engage, not wanting to you know, eval their businesses because that'll provide the

evidence that their value chain is being automated. And there's others that I mean, literally,

like, you know, some of the most recognized sophisticated Fortune 500 businesses that that have

this mentality. And there's others that are leaning into it of if we have the

ability to do 10 or 100 times more, what will that mean? And how do

we lean into that future? Because there's so many things that going to change over

the next 10 years. And I think those are the kinds of businesses that are

going to be successful. Let's talk about labor markets more broadly. You guys, so it's

interesting though, you started not feeding people to AI labs, not training models.

It was just like help people find jobs, help companies hire. And then you're like,

oh wow, this whole opportunity. You have this really interesting view on the future of

just labor markets and hiring. Talk about that. Yeah, it's interesting. I remember

When we started the company, as I mentioned, we were 19 and just had this

like gut intuition that it felt so wildly inefficient that labor markets are so

disaggregated. And what I mean by that is when we would hire someone internationally, they

would apply to a dozen jobs. When we as a company in the Bay Area

were considering candidates, we would consider a fraction of a percent of candidates that were

available in the market. And the reason for that is that there is this matching

problem that everyone's solving manually where they'll manually review resumes, they'll manually conduct interviews

and manually decide who to hire. But when we're able to automate that matching problem

at the cost of software, it makes way for this global unified labor market that

every candidate applies to and every company hires from facilitating a perfect flow of information

in the economy. And I think that that future is undoubtedly what we're heading towards.

But what we've realized over time is that the nature of work is also changing

dramatically. And part of building that future over a 10 year time horizon is like

creating that future of work. And all of the more tactical things we do and

building these incredible data sets across evals and RL environments for our customers.

What I've seen in how hiring has changed, I'm doing research on this with a

partner, Noam, It's so much easier to apply for companies that everyone's just applying now

to hundreds of companies. AI is just making it easy to adjust their resumes and

cover letters and make it feel like, oh, I applied to more of course very

specifically, but it was one of a hundred places. And then on the flip side,

hiring managers are getting flooded with applications. And so now they need AI to filter.

So even if we didn't want to get to this place, we're almost being pushed

into this direction of so much volume on both sides. We need something really smart.

at filtering and helping us hire and selectiveness is exactly what you guys have been

building for a long time. Precisely. Yeah. And the fascinating thing, like a lot of

people ask, are we, do we think about ourselves as a labor marketplace or do

we think about ourselves as a data company? And I think that the reason it's

an interesting question is our realization on the, from what the

labs need is that they actually need a labor marketplace. They actually need these exceptionally

high caliber people. And of course, well, you know, layer on some project management and

some software platform associated with it. But the really core thing that they want is

how do they find these extraordinary professionals across all of these different domains that can

measure model capabilities and work to build that future work together? This

episode is brought to you by Interpret. Interpret is a customer intelligence platform used by

leading CX and product orgs like Canva, Notion, Perplexity, Strava, Hinge, and Linear to

leverage the voice of the customer and build best-in-class products. Interpret unifies all customer

conversations in real time from Gong recordings to Zendesk tickets to Twitter threads and makes

it available for your team for analysis and for action. What makes Interpret unique is

its ability to build and update a customer-specific knowledge graph that provides the most granular

and accurate categorization of all customer feedback and connects that customer feedback to critical metrics

like revenue and CSAT. If modernizing your voice of customer program to a generational upgrade

is a 2025 priority, like customer-centric industry leaders like Canva, Notion, Perplexity, and

Linear, reach out to the team at interpret.com slash Lenny. That's E N T E

R P R E T.com slash Lenny. Going back to just how this all works

and what you guys do for models. I was talking to a friend who had

an ankle sprain or his foot was hurting and he got an x-ray and he

fed the x-ray into Chachapiti and then asked him like, give me this specific x-ray.

And he's like, okay, sure. And then he gave him, here's what you have. And

he was talking to me. He's like, how did, what is out there on the

internet that trained this model to know this stuff? And I was like, no, it's

actually somebody sitting there helping the model understand this once they recognize it doesn't

fully understand this. Like humans are actually helping them learn these things. Exactly. Well, so

the way it works, at least what most people's understanding is, there's a lot of

complexity in how the models work, is that pre-training gets a lot of the knowledge

into the model of what are all the different things that sort of see it

in the world. And then post-training and reinforcement learning is for all the reasoning of

what are the pieces of knowledge that are accurate, what are inaccurate, and what to

prioritize at any given time to make a decision. And so behind that, there would

have been radiologists that worked on the post-training data set to create some stasis point

for here's the diagnosis and rewards and penalties associated with it. And it's really the

quality of those people that went into the quality of the decision and recommendation that

ChatGPT ultimately made. So let's actually follow that through, because that's really interesting, and I

don't know how many people understand it. I sort of understand it. So the work

that you do and these experts do is post-training. It's not feeding data into the

model that it's trained on. It's we have this model GPT-5. Now here's all the

things it's missing. Let's add to it. Exactly. Yeah. It's really unlocking,

allowing the model to focus on all the right tokens from pre-training all the right

things in model context, upweighing the effective reasoning chains to... enable the

models to reason better in a more generalized way. What's the scale of people just

working on the stuff is like thousands, tens of thousands, hundreds of thousands? Tens of

thousands at any given time, hundreds of thousands more generally. I mean, it's huge. And

the most exciting thing is that it's growing really quickly. I mean, I think that

to your question also about the competitive landscape, historically, there are were all these crowdsourcing

companies that would get these super high volumes of low skilled people. I think like

Scale and Surge were the primary companies that pioneered that industry. And then in this

transition to higher skilled labor, what people realized is that actually you can go a

lot further with just getting higher caliber people, even in smaller amounts

initially. And now subsequently scaling that back up once they're able to meet the quality

bar. And I think that There's a bunch of companies that after our success and

very rapid revenue growth that sort of started early last year have chased after that,

which makes sense, right? And seeing that the market was changing very quickly, we were

taking off and trying to pursue a similar thesis on the market. It's interesting. There's

always been these companies, AlphaSites and GLG, that like sort of did this before AI

or is like paid to connect to an expert and ask them questions about stuff.

And essentially, okay, it turns out this is really useful for models. We don't need

the person in the middle. Exactly. Yeah. Well, but one core difference is that alpha

sites would generally be a one-off call versus a lot of our work is really

hiring people for projects, right? Of how do they work on something for a longer

period of time? And so that's, I think, one of the reasons that some of

the traditional expert networks have struggled to get into this. And also, how do you

retain those people and think about all the incentives where it actually looks more similar

in some ways to one of the traditional labor marketplaces of an Uber or DoorDash,

just with much higher skilled talent that's treated exceptionally well. It's such a good opportunity

for me to learn so much about this. So I'm just going to ask questions.

Yeah, so interesting to me. How much of the experts are focused on

specific concrete knowledge versus personality? and like softer skills? How much of it is like,

here's how you do an exam, here's how you do an x-ray? It depends on

the lab. It's a lot of both. I think that previously it might have been

more softer skills, but now a lot of the labs are focused on their business

models of what are the economically valuable capabilities that drive revenue and leaning a lot

into these professional domains. But I think the creative side is also still really important

to everyone. And so we're seeing a meaningful amount of both. Like we hired all

the people from the Harvard Lampoon a couple of months ago, their comedy club to

help with making models funnier. And so do all sorts of stuff like that, hiring

Emmy award-winning screenwriters and everything across the board on creative capabilities that you'd look for.

That is amazing. What a cool story. Yeah. I'm excited for this to kick in.

How fast do these things turn around? Like say you hired this team, like how

fast are we going to see the impact potential? Is it like months? Is it

years? Well, so it depends because some models or some labs will release iteratively

where they'll just improve the model behind the scenes sort of every. Without announcing a

new model. Exactly. As for every couple of weeks versus others do these big releases.

And so it depends a lot. We're behind all of them. But I mean, we

move really fast. It would be a customer gives us a request of we need

these, you know, award winning screenwriters. And within 24 hours, we'll turn around

the experts. And there's also this really interesting dynamic where in a set of 100

people that we hire, oftentimes the top 10% of people will drive majority of the

model improvement. It's sort of like a company, right? If you have a 100% company,

oftentimes the top 10% of the company will drive majority of the impact.

And what that means is that when we're able to build proprietary advantages and identifying

who are those top 10% of people, both insofar as how do we have them

on our platform, but also identify and match them effectively, it creates so much value

for customers that it's difficult to compete against. And so it really does tie back

to the founding thesis of the company, which is, Like, you know, how do we

find these extraordinary people and identify them so that we can reliably deliver these

top 10% or top 10x experiences for our customers? So on that, so

is the idea you hire Jane, she's incredible at coding, and she now works for

Anthropic, and that's her full time job doing this? Or is this like a part

time thing? Is this a project thing mostly? It would sometimes be part-time, sometimes it

would be full-time. I would say most often it's part-time where it's like, you know,

someone might work at a fan company where they're underemployed, maybe one of the ones

that's moving slower where they have an extra 20 hours a week and then they're

able to do this on the side or, you know, whatever the equivalent is sort

of across a bunch of different industries. But we also do a lot of, you

know, 40 hour a week roles as well. And how much are they making? Is

it meaningful enough for a FANG engineer to spend time on this? Yeah, very meaningful.

I mean, so our median pay rate in the marketplace is $95 an hour, but

it can flex up well up into like $500 an hour based on the depth

of someone's expertise. And one thing that highlights this difference relative to a lot of

the crowdsourcing companies is if you look at the economics of the crowdsourcing companies, oftentimes

they would pay like $30 an hour to town as sort of the average. And

so think about the, you know, people that you can hire, the undergrads for $30

an hour versus the, you know, Goldman bankers, the McKinsey analysts, the

FANG software engineers. And ultimately it comes down to what are the capabilities that

labs want their models to have? And it much more falls in the latter bucket

than the former one. I know there's only so much you can talk about with

this stuff, but So Anthropoclawed has been so good at coding so much better

historically than other models. I also use it for writing, giving feedback on writing.

What is it that allowed them to get so good at this and continue to

be so good at this? Well, I can't go too much into detail about customer

work, but I think that it's this trend of reinforcement learning and

being very thoughtful about defining the right rewards that we're really seeing across the board.

and how we can mitigate reward hacking, set up the right rewards, that's super

impactful. EVALS, again, EVALS is all you need. Back to EVALS. One of my favorite

quotes from customers is that models are only as good as their EVALS, which has

always held true. I think Greg Brockman tweeted this once, EVALS are all you need.

Yeah, no, truly. Let's talk about Mercor a little bit more. One of the maybe,

not even maybe, I believe, The data tells us it's the fastest growing company in

history. Yeah. I want to understand what you did to make this happen. So

let me just ask, what do you think are some of the core tenets of

how you built Mercord that most contributed to being this successful? I think the most

important thing is looking at the leading indicators in fast moving markets.

Like I remember when I used to think everyone in venture talks about the why

now. And I used to think about the why now of how from a product

standpoint, less from a market standpoint of like now we can automate the way that

we review resumes or the way that we conduct interviews, et cetera. But ultimately, like

there is this legacy market that's, you know, has all these incumbents and it's relatively

stagnant. What matters a ton is actually figuring out what are the new markets, the

new pockets of demand that are changing very quickly where, you The wealthiest

customers in the world are willing to pay whatever it takes to improve model capabilities.

And how do we focus on the leading indicators of those markets to make sure

that we have the best solution for the flagship customers, you know, in the

market and optimize everything around that. And that's what I found has been most impactful

in building the business. I think that's maybe that's one thing is like leading indicators

and markets. If I had to choose another, it's customer obsession.

We're starting to have a couple of product managers

help out with go to market, but for the last year and a half of

the business, we've had no one in sales and marketing. And so we're sort of

immature from a sales and marketing standpoint because we focused 100% of company resources on

how do we build great products and experiences for our customers. You know, just getting

word of mouth that the people that have worked with us at other businesses want

to keep working with us and leaning into creating those great experiences. And so that's

where I spend all my time. And I think that some founders can get caught

up in like, how do they get really good at marketing before they've figured out

the thing that really drives a lot of customer love and creates the six

star experiences that you're used to building. I want to go back to that first

point, which is like, okay, you found this pocket, maybe the biggest business opportunity in

history. How did you first find, what was that moment of like, wait, this could

be, this could be really big. So there's some crazy stories here. I remember we

started the company, as I mentioned, in January, 2023. And then in August, 2023, when

I was still in college, one of our customers introduced us to the co-founders of

XAI over a Zoom call. saying how we had these really smart Indian software engineers

that were great at math and coding. So we met them and we explained how

the software engineers we had were really good at math and coding because they weren't

distracted by all the humanities. They didn't have to study history and English and all

these other things. And they loved it, right? So they had us in two days

later to the Tesla office. And we met the entire XAI co-founding team, except for

Elon, while I was still a college student. Right. And actually, I was just getting

started at that point. And they were super excited about our focus on the quality

of the experts. And so while they were still doing pre-training, they weren't ready for

human data at the time. And we didn't start working with them at that point.

We just knew from that point forward before we even dropped out that the market

was about to change radically and we needed to be at the frontier of that.

And so then fast forward a few months. one of the crowdsourcing players came to

us and actually used our platform to hire over a thousand people, where this is

very interesting experience because we started getting flooded with support tickets about how those people

weren't getting paid. And we obviously felt horrible because we had referred them to this

opportunity. It was this like reputable company. And we realized

that a lot of the incumbents were resting on their laurels with respect

to what was needed and the experiences they were creating for talent in their

marketplaces to help improve models. And there was this opportunity to work directly with the

labs in a way that kept the dignity of the experts in the marketplace,

paid them extremely well and sort of cut out the middlemen. And so we started

doing that in May of last year. And then the rest is

history. Wow. Hundreds of millions of dollars in revenue since. So what I'm hearing

here is you were very open to looking for pull. You saw some pull, you

explored it. And then once you saw that there was something really meaningful there, you

just went deep on making that an incredible experience as amazing as possible. Exactly. I

think like if I had to distill it into advice for founders, one thing I've

realized is that I spent a lot of time trying to like force product market

fit. And in some ways, you should be persistent. You should have these theses that

you have conviction about how the world will change. But sometimes you just need to

hear it from the market and know that it's there, the pull, to know the

right places to focus. Because if it's difficult to sell, if it's extremely difficult to

sell the marginal customer, you're not going to be able to grow a huge business.

What you actually need to find is the customer that's surprisingly easy to sell into

is where you're going to be able to grow with them. You know that it's

a large pain point. And so it's some combination of being stubborn with respect to

your thesis around how the world will change, but also very open-minded with respect to

exactly what form that takes and how the market's developing and how your company will

fit into it. That's an amazing insight. In the moments you described, it felt like

it was a combination of this XAI meeting, feeling like, oh, wow, they really, really

want this thing that we sort of have. We're not doing an amazing job at

it. And then it's a thousand people higher in the platform. Was it those two

moments that are like, wow. Exactly. And those happened, keep in mind, while we were

a seed company, right? Well, so the first one was before we even raised any

seed funding, we were totally bootstrapped because we bootstrapped the company to a million dollar

revenue run rate and have always remained super capital efficient. Like we've never burned money.

We were lifetime profitable. And then we raised our seed round in

September from General Catalyst. And it was the other experience after we raised our seed

round where we really knew that there was an enormous amount of demand in this

market where we saw the volume, right? And we saw that the incumbents were sort

of sleeping with respect to how the market was changing and the kinds of people

that were needed to make that change happen. It's one thing to see this opportunity

and start to execute on it. It's another to actually succeed at this scale and

consistently win. You guys have very specific values within the business. Talk about those. It

feels like that's a big part of your success too. It totally is. So I'll

give the three and maybe a brief story associated with each of them. So the

first one is having a can-do attitude, which everyone gives me a little bit of

a hard time for because it's sort of a funny saying, but we've always set

these ridiculously ambitious goals and somehow the trajectory of the

company forms around those goals. Where I remember when we were talking to Benchmark, before

they led our Series A, we were at 1.5 million in run rate. And I

said we'd be at 50 million in run rate by the end of the year.

And they said we were absolutely insane, right? As anyone would. And plus, there might

be two weeks we hit it, right? And then we've now well blown past, you

know, the tracking to 500 million in run rate, which was initially our goal for

this year. So setting these incredibly ambitious goals with respect to The revenue scale of

the business, the caliber of experiences for talent, all those dimensions are super important to

first have a can-do attitude. The second thing is really high standards, which is who

we hire and what we expect of them. Like we have an incredibly high hiring

bar where we hire tons of former founders, people that have incredible experiences. We just

hired or partnered with Sandeep Jain, who joined us as president. He was previously the

chief product officer and chief technology officer at Uber. and joined our small,

relatively small in the grand scheme of things company to help scale up all the

processes where Uber is, of course, the largest labor marketplace in the world. So super

high standards is of paramount importance. And then the third one that we really lean

on significantly is intensity. And that if you look at the early cultures of

businesses, of the legendary companies, thinking of Meta or Google, they have these

incredible, intense early stage cultures of people just moving heaven and earth and doing whatever

it takes to push the frontier of model capabilities. And so still very much output

oriented of what do people achieve rather than input oriented of the specific hours they

work, but recognizing that it takes a lot to build a legendary

business. And that's ultimately what we're optimizing for. I could see why this works. Can

do attitude plus high standards plus intensity. Uh, I could see how that leads to

success. There's a lot of talk these days about this 699 culture working six days

a week, 9 a.m. to 9 p.m. You know, a lot of people are like,

why? That's terrible. Why would you make people do that? But at the same time,

I'm just constantly hearing this from the most successful AI companies. This is just the

way it is to be successful. Things are moving so fast. This is an opportunity

you'll never see again. Just talk about your thoughts on that. Yeah, well, to clarify,

we've never mandated hours. It's more of a byproduct of people that care a lot,

where we care a lot about the trajectory of the business. And so a lot

of people come into the office and stay late. But, you know, if they need

to leave early and get dinner with their kids or, you know, travel on the

weekend, of course, that's totally fine. And for us, it's much more about finding people

have a lot of ownership and are really bought in. less so about the specific

hours in the office, even though we found that oftentimes it's the people that are

most bought it, not always, but oftentimes it's the people that are most bought it

and that, you know, sort of burn the midnight oil with us. When you say

high standards, is there something you could share that gives us an example of what

you mean there? Because a lot of people think they have high standards and they

don't. If you are very patient, there's always some trade-off between speed

and quality when hiring. And I remember, especially for our first 10 people, we

were just so patient and disciplined about finding some of the best people in the

world. Like, you know, half of them are for our second employee, Sid, as an

example. Our second employee in the in the US, Sid, was previously the head of

growth at scale, you know, who joined us when we were a seed stage company.

Daniel, who joined us, was previously scaled to consumer apps to over 100,000

users. And all sorts of just like extraordinary backgrounds of our

first 10 hires. And I think that that initial talent density shaped so much of

what the rest of the org looks like as you scale it out. I know

you also have this perspective that people talk about waiting to hire timely

slowly, but it's actually not necessarily the right advice. Talk about that. It's painful because

it's a double-edged sword. Like on one hand, I'm thrilled that our first 10 people

are like so phenomenal. And I think that that has paid dividends for the business.

But on the other hand, I think that companies do get to the point where

you just need to hire really fast. And there are some things where you need

a lot of people to do them. And you need to recognize that there's gonna

be some variance associated with hiring, but moving quickly is the priority. And I think

that in some ways we move too slowly with how we scaled out the team.

And so the benefit is that everyone is extraordinary. We have this super high bar

and we want to maintain that over time. But I think the downside is that,

you know, while the company has grown incredibly quickly, we likely could have grown even

faster if we had moved a little bit more quickly with especially

ramping from call it like 10 to 100 people. OK, I was going to ask,

so it sounds like the first 10, be very careful, take your time. 10 to

100, maybe speed up a bit. Yes, though I wouldn't say it's necessarily 10. It's

determined by the point where you know it's really working. And I know that's still

not like a bright line, but it's like once you know that there is so

much more demand than you can handle, that's when you want to step on the

gas and optimize for speed in a lot of ways. But I think especially until

then, it's important to be patient, be disciplined, get the best people is always

important. But speed becomes more important once you find the

market opportunity, the market vacuum. I know you've started a couple of companies in the

past, much smaller scale. In this new role as CEO of this massive hyper

growth company, what surprised you most about where you spend the time most or just

what the role involves? Because a lot of people want to start companies, dream about

being in your shoes. What are they maybe not understanding about where a lot of

your time goes? Yeah, it's actually not too surprising. Like the top two buckets are

always working on hiring and time with customers. How do I really

deeply understand what customers need and how we can support them? And then how do

I build the team and a lot of the processes around that? Of course, there's

all of the ad hoc things I didn't expect of dealing with the people

questions of how do we set up our levels and our comp bands and all

of that, which you sort of learn as you scale a business. But I think

that the core places that I spend my time are in line with

what I expected as well as what I love doing, which is very fortunate.

So these two companies you've started in the past, maybe share what they were, because

they're fun. And then how do they help you be successful in this? Like what's

something that they taught you that helped you in your current role? Yeah, so there's

been like a dozen, but I'll choose my favorite too. So when I was in

eighth grade, I started Donut Dynasty, where I saw that Safeway donuts were selling for

$5 a dozen. And I was amazed, because I felt like as an eighth grader,

this was such an incredible deal. And so I started to bike down to Safeway,

buy Safeway donuts for $5 a dozen, and then go back to my middle school

and then sell them for $2 each, running really good. Margins, of course. It sold

out super quickly, and so then I need to scale up. So I would pay

my mom $20 to drive me in her minivan down to Safeway, buy 10 dozen

donuts, go to my middle school, sell them all out. And then the school tried

to shut me down. And so because I was selling like food on school campus,

which they didn't like, so they had me in the principal's office asking me to

not do that. And then I moved my donut stand over 50 feet. So it

was off school campus saying that they could no longer, you know, police me. I

remember we had competitors pop up where the competitors were charging. They bought these Chuck's

donuts, which if anyone in the Bay Area knows are you know, higher end donuts

than Safeway donuts, but they have a higher cost basis. They cost a dollar per.

And so I dropped my prices to $1 for two weeks to run them out

of business before I knew what anti-competitive practices were. And I'd hire all my

friends, paying my friends in donuts because, you know, they perceived the donuts as $2

each where they could sell them throughout the school and I could have a lower

cost basis on them. So I had all of these like fun experiences in selling.

And then I could talk more about my high school business as well, which was

a more significant scale. But I think the takeaway from that was just like, you

can just do things like so many people have ideas, but the barrier to more

companies being built, I think is just initiative and taking the steps to build the

product or experience that customers want and investing the time and the

ambition to scale that up. And so I think it was really getting reps of

that that enabled me to realize that I should do it later on

at a much larger scale. Amazing story. I love how wholesome that is versus like

drugs. Then my mom was very worried. She was like, oh, is there any pot

of these donuts? I was like, no, mom, I assure you, these are pure donuts.

I love that you paid your mom $20 to drive. She was adamant. It couldn't

be a handout that she was taking her time to drive these. So she needed

to make a little bit of money off of it. We haggled over her title

where she Eventually she wanted to be head of global operations, which we thought very

entertaining. I hope that's on her LinkedIn. Not yet. Maybe she'll have to add it.

So you said that you've started a dozen companies. Yeah. Wow. OK. Well, a dozen

projects, but I think it was that and then my AWS company were the two

that that I sort of scaled up. What's the story behind Mercor as the name?

Mercore means marketplace in Latin or to buy, sell, trade. And we want to build

the largest marketplace in the world, the marketplace for how everyone finds jobs. And

that was really the draw to it. Okay. Maybe a last question. This is going

back to earlier in discussion because it's something I've been thinking about as we're talking.

There's been this shift from data as kind of the fuel for models, and now

it's experts. Do you think there's a next step or is this just like, we'll

take us to AGI superintelligence? I don't think it's necessarily changing from data to

experts. It's more just the paradigm of realizing that labs need this

close collaboration with experts to help understand what are the

evals that they're building and how can they push the frontier. But I think it's

very clear that evals are evergreen, that So long as we want to improve models,

we'll need experts to create evals for them and to create the post-training data for

them to learn those capabilities. And of course, there might be changes in the exact

way that people do training with RL or otherwise, but they will always need an

eval to measure what does success look like across every domain that they want to

build. Okay, so then building on that, a question that comes up a lot these

days is, and I know we're talking about fun stuff, but I'm getting to serious

stuff again. scaling laws and just like progression of model intelligence. A lot of people

are feeling like, I don't know, it's slowing down. We're not going to really get

to super intelligence at this rate. What is your sense? I totally agree with that.

Like, I don't think it's, I know there's been some executives at big labs that

say we'll have super intelligence in three years, but I think the truth is that

it's a longer road. And that's not to diminish from how extraordinary the models are.

Like, I think we'll be able to automate a majority of knowledge work tasks in

the next 10 years for sure. But that long road is paved with all of

the evals that help to make those capabilities possible. And it's not going to

be 10x more pre-training data that gets those capabilities. It's much more going to be

all of the host training data sets that are far more data efficient and thoughtful

that help us get there. David Sacks tweeted this interesting point that Like the situation

we're in now is almost the best case scenario where AI is not in this

fast takeoff to super intelligence. There's a lot of competitors kind of keeping each other

in check. Models are already very valuable and only getting valuable, more valuable. But there's

not just this like winner super intelligence taking over the world situation. Yeah, I think

that's true. I think a lot of the super intelligence fear mongering is

probably overrated. But at the same time, a lot of people's framing around that is

even if there is a 5 to 10% chance of this P-Doom, then we should

be careful, which seems logical. But I think that it's going to be an

extraordinary 10 years for all of Silicon Valley and all of the world as this

technology is able to create abundance and giving everyone better medical

treatment, the best access to legal recommendations and the ability to build great

products more than we've ever seen before. And education feels like is transforming. Absolutely. Right.

Like I even have felt bits of this over the last 10 years where like

I remember ever my parents would give me a hard time for not going to

classes in college. And I'd be like, well, there's way better lectures on YouTube. Why

not just listen there? But I can only imagine as the models get

extremely good at conveying information, better than the best professor, what that'll mean,

right? And access to all sorts of information to better forward

humanity and upskill everyone. So I'll use that as a segue to a final question.

I'm going to take us to AI Corner, which is a recurring segment on the

podcast. What's some way that you personally use AI to do better work, to help

you in life? Well, let's see, I use it a lot to write documents, as

you would expect. also talk to it to get advice on problems. Like I find

it helpful to just reason through almost as a thought partner. Because, yeah, I don't

know. I find I think better sometimes when I'm talking something through, but I can't

talk through everything with colleagues or people around me. And so this is like chat

GPT voice mode mostly or something else? Yeah, I like chat GPT voice mode a

lot. There's definitely room for improvement, but I am very excited about the future of

voice. Let me show you something I built, actually. I wasn't planning to talk about

this, but There's this guy, Eric Antoneau, who's been recommended by a lot of people

to get him on this podcast. He's this creative product person that's kind of under

the radar now. He's on Facebook for a long time. He built this project called

Parrot GPT, which is you basically put JetGPT into a stuffed animal to talk to.

So built a little wise owl. I don't have it on right now. But basically,

you sew in a little speaker right here, and you put a little magnet underneath.

And you can put it on your shoulder and then just talk to it. Wow.

I love it. I'll have to get one of those. Because I have some of

the voice assistants in my apartment, but I really want a ChatGPT voice assistant. And

so I'm excited for that. I was just thinking of that. Like, yeah, just come

on. Why can't we have a ChatGPT voice just sitting around listening to us all

the time? And you can on your phone because it goes to sleep and it's

like, hello, what? Exactly. Yeah. Yeah. So it's kind of what this is trying to

be. Well, there's a Kickstarter. He started that we'll link to that you can help.

There we go. It's really easy. Brendan, is there anything else that you wanted to

share or touch on or maybe leave listeners with before we get to our very

exciting lightning round? Tying to the point around initiative and that you can just do

things, I encourage everyone, especially with AI and it being so much easier to build,

just take the initiative to go out and build products and talk with

customers and take that leap of faith because I think that that is in so

many ways the largest barrier to more innovation in the economy in any way that

we can support that. Yeah, there's so many people that just, nothing, let's not bash

the podcast, but just listen to podcasts, read posts, just keep reading and listening and

don't do anything with that information. And there's never been an easier time to actually

build stuff and try stuff. So definitely take that advice. Just you can do things.

You should move your donut stand 50 feet and get out of their jurisdiction. Okay,

Brendan, with that, we've reached a very exciting lightning round. I've got five questions for

you. Are you ready? All set. What are two or three books that you find

yourself recommending most to other people? Let's see. I would say In Order, High Output

Management is a phenomenal book on running companies. Second is Zero to One, which of

course is a classic. And then third is Shoe Dog, where I just find it

to be a really inspirational story. What is a recent movie or TV show you've

really enjoyed? I really liked... Oppenheimer. My favorite TV show of all time is Suits.

So I know not recent, but if I had to choose a recent one, probably

Oppenheimer. Very cool. Suits, first time someone's mentioned that. Favorite product you recently discovered

that you really love? I love using Codex, like the new version. I know

it's sort of new in terms of version. Yeah, I think it's incredible and just

a huge, huge improvement. So yeah. Do you have a life motto that you

find yourself coming back to, sharing with folks, finding useful in work or in life?

I think it's you can just do stuff. You know, what we were talking about

earlier, take the leap of faith. I thought you were going to say can do,

which is in your Twitter profile. It could be can do as well, yeah. Two

great ones. Final question. So we were chatting before this about things that we could

talk about, and you share this interesting thing that you haven't shared anywhere else, which

is that you're dyslexic. Mm-hmm. Why don't you share that with folks? And just how

do you get around that, having built the fastest growing company in history? I don't

hide it at all. Like I think a lot of my colleagues know. And I

think on one hand, it definitely makes it difficult to go through a thousand emails

a day or read every document that I'm supposed to. But on the other hand,

I feel like it helps me to think a little bit differently, to be more

creative and perhaps see the ways that markets are changing that not everyone sees.

And so it's turned out okay so far. And so, you know, I

try to, I think one thing it's helped me realize from a management standpoint is

that we focus much more on how we can leverage people's strengths rather than helping

to improve weaknesses. Because there's some things that I'm not great at and I'll never

be the best in the world at. And there's others that I can hopefully refine

and strive to be. That's such a also recurring theme on this podcast of just

focusing on strengths and not over not focusing over all your focus on weaknesses. Brandon,

this was incredible. I learned so much. I have a billion more questions, but you

got shit to do. Two final questions. What should people know about what you're doing

and roles you're hiring for? And then how can listeners be useful to you? Absolutely.

We're hiring a ton across the board on our team. We're hiring strategic project leads

on our operations team, software engineers and our engineering team, as well as researchers. And

so please go to Mercore.com and we would love to work with you. And that's

the largest way that you can help us. Share it with your friends as well.

Over half of people in our marketplace come from referrals because we have a platform

of people that love us. And so any jobs that you want to apply to

or send your friends to, we'd love to have you. Brandon, thank you so much

for joining me. Thank you for having me. Bye, everyone. Thank you so much for

listening. If you found this valuable, you can subscribe to the show on Apple Podcasts,

Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving

a review as that really helps other listeners find the podcast. You can find all

past episodes or learn more about the show at Lenny's podcast dot com. See you

in the next episode.