Mercor CEO Brendan Foody on $2 billion valuation, streamlining hiring with AI

260,000ft!S.

>> New. This morning, AI startup Mercor announcing that it has raised $100 million in a series B funding. The company, now

B funding. The company, now valued at $2 billion, is up from just $250 million four months ago. Quite quite the jump.

ago. Quite quite the jump.

Joining us right now first on CNBC is co founder and CEO Brendan Foody. Good morning to

Brendan Foody. Good morning to you. Congratulations on the new

you. Congratulations on the new valuation and the new funding round. For those that don't

round. For those that don't understand sort of how you're using AI in the context of employment, help help the audience understand what's happening here.

>> Yeah of course.

>> And first of all, thanks.

>> So much for having us back on, and we're. Excited to.

>> Discuss the. New funding.

>> And update on the business.

>> But at a high level, macaw is training models.

>> That predict.

>> How well someone will perform on a job better than a human can. So similar to how a human

can. So similar to how a human would review a resume, conduct an interview, and decide who to hire, we automate all of those processes with Llms and it's.

>> So effective.

>> It's used by all of the top AI labs to hire thousands of people that train the next generation of large.

>> Language models.

>> And just to be clear, is it better for specific for identifying specific types of jobs, specific kinds of roles, meaning for an engineer or for some kind of other role? Can it

capture, you know, how well an employee will do in a creative environment, for example, or at a senior management role? Would

you use this technology?

>> Yeah. Phenomenal question.

It's really across the board.

>> But what.

>> We excel at is analyzing things that are based in text.

>> So for.

>> Example, analyzing an interview transcript.

>> Is.

>> Much easier for us than analyzing what.

>> Is someone's. Persuasiveness

in a. Sales demo or a sales conversation. But that.

conversation. But that.

>> Is.

>> Broadly applicable to.

>> Software engineers.

>> Doctors.

>> Lawyers, accountants. reallya

huge. really a

huge.

>> Portion of the knowledge work economy, all of.

>> Which are.

>> Domains that. We're already

actively hiring many hundreds of people in. So how does it work t

people in. So how does it work t talked about, for example, analyzing a transcript from a job interview, are you also giving me if I was interviewing a prospective employee, are you

giving me a script that I'm following?

>> So we would ask dynamic questions of people based on their.

>> Background where.

>> Similar to.

>> How a.

>> Human would have.

>> Context on your resume.

>> We would.

>> Ask questions.

>> About that.

>> And they.

>> Would respond and we.

>> Would ask follow ups based on their responses, etc. One thing, one thing I will.

>> Clarify based on the customers.

>> Added on screen.

>> Is. That we can't confirm customer names.

>> But we.

>> Do work.

>> With all of the top.

>> AI labs.

>> And most of the largest technology companies in the world, and your your models based on open source other models. Meaning are you sort of

models. Meaning are you sort of taking a, you know, some piece of llama, for example, or even deep sea now and integrating that into your own model? How is

it, is it totally custom from the ground up? How did you do this. Yeah. Phenomenal question.

this. Yeah. Phenomenal question.

So we post. Train other models.

>> It doesn't make sense for us to do things custom.

>> From the. Ground up. But most

importantly what.

>> We look at is.

>> The performance data of how people.

>> Do on projects where.

>> We're.

>> Able to see who's doing.

>> Well.

>> Who's not.

>> Doing well.

>> And understand all of the reasons. Behind that, so that.

reasons. Behind that, so that.

>> The model makes better.

>> Predictions around.

>> Who's going to perform well.

>> And for what reasons, in terms of.

>> Which base model we use. It

depends on the specific.

>> Use case.

>> But have.

>> Had a lot of success customizing.

>> The OpenAI API as well as many others. In terms of this,

many others. In terms of this, this new funding round, what are you going to be using this this cash for? What happens next?

cash for? What happens next?

>> Yeah, it's an interesting question.

>> Because the.

>> Business has remained quite profitable. So we've been we've.

profitable. So we've been we've.

>> Grown an.

>> Average of 51%. Month over

month in.

>> Revenue for.

>> The. Last six months and are still.

>> Profitable with a revenue.

Run rate over.

>> $75 million.

>> But the.

>> Main motivation of raising the. Capital is to invest in two

the. Capital is to invest in two things. The first is the supply.

things. The first is the supply.

>> Side of our. Marketplace and,

you.

>> Know.

>> Getting. All of the smartest people in the world on the platform and continuing to support our.

>> Customers that way. And then

the second is. Our AI.

>> Vetting technology.

>> And making.

>> Better.

>> Performance predictions so that we.

>> Can have.

>> Higher confidence.

>> In every.

>> Individual that we're matching with our customers.

>> On projects.

>> Brendan, let me ask you a question that I imagined the venture capitalists who put money into this thing, or maybe those who don't would ask, which is what is the defensive moat around a business like this? I

think to myself, you know, LinkedIn should be doing this, which of course is owned by Microsoft. And given their

Microsoft. And given their access to OpenAI and everything, how how do you think about the competition in this space long term?

>> Yeah. So I think the two moats are tied to the two things that we're investing in. The

first, in terms of supply side growth, getting all the.

>> Smartest people in the world on the platform.

>> Is reflective of the supply.

>> Side marketplace. So similar

to how you would. See a

marketplace.

>> Moat and.

>> An Uber or an.

>> Airbnb, those are some of the strongest.

>> Most enduring network effects in the world that.

>> We're continuing to build.

>> Out.

>> Incredibly quickly. But the

second.

>> Moat.

>> And the one.

>> That I'm arguably.

>> Even more.

>> Excited about.

>> Is the data.

>> Flywheel.

>> Where we learn.

>> From every.

>> Customer interaction about.

Who's doing well, for.

>> What reasons, about the features that indicate that, whether it's because someone.

Has a certain experience.

>> On the resume or.

>> They.

>> You.

>> Know, demonstrate extreme passion for that topic area in their interview or even specific knowledge, and we're able.

>> To.

>> Learn from all of those things to make better predictions.

>> For every future person we.

>> Onboard, not just for that.

>> Customer, but.

>> For all of our customers.

>> More generally.

>> I got one final crazy question for you. First, I know this. I know this apparently

this. I know this apparently works for employers. Could you

ever imagine higher education using this?

>> Absolutely. I mean, I think that.

>> How do you think that kids I mean, all of these I'm just thinking of all these these admission programs all over the country at colleges and whether this would change the whole whole thing. And then I but I

whole thing. And then I but I don't know if I want that or not, because what about the kid who doesn't do well on the test, but also but does, you know, brilliantly on something else?

>> Yeah. But I think the key thing is that the baseline is so bad. Right. Like right now we

bad. Right. Like right now we have so much of this vibes based assessment.

>> We have a lack of data in driving the.

>> Outcomes that we.

>> Care about.

>> That it just makes sense to, you know, look at the things that drive the outcomes that we.

>> Care about, whether.

>> It be.

>> You know, people having a well rounded education environment or the outcomes of those.

>> Students once they enter.

>> The workforce or whatever it is.

>> And I think it's. Almost

certain that.

>> In the.

>> Imminent future, all.

>> Of this.

>> Will. Converge towards a complete, completely automated process.

>> Not only because.

>> It's dramatically.

>> Cheaper, but more importantly