From Job Displacement to AI Trainers, Brendan Foody on Work in the AI Age

**Sarah** (0:05)
Hi, listeners, and welcome to No Priors. Today, we're chatting with Brendan Foody, co-founder and CEO of Mercor, the company that recruits people to train AI models. Mercor was founded in 2023 by three college dropouts and Teal Fellows. Since then, they've raised $100 million, surpassed $100 million in revenue run rate, and are working at the top AI labs. Today, we're talking about where the data for foundation model training will come from next, evaluations for state-of-the-art models, and the future of labor markets. Brendan, welcome to No Priors. Brendan, thanks so much for doing this.

**Brendan Foody** (0:36)
Yeah, thanks for having me. Excited to be here.

**Sarah** (0:37)
So, you guys have had a wild last six months or so. There's huge traction in the company. Can you just talk a little bit about what Mercor does?

**Brendan Foody** (0:47)
Yeah, so at a high level, we train models that predict how well someone will perform on a job better than a human can. So, similar to how a human would review a resume, when they 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 models.

**Sarah** (1:09)
What are the skills and job descriptions that the labs are looking for right now?

**Brendan Foody** (1:14)
It's really everything that's economically valuable, because reinforcement learning is becoming so effective that once you create evals, the models can learn them and how to improve capabilities. And so for everything that we want LLMs to be good at, we need evals for those things. And it ranges from consulting to software engineers, all the way to hobbyists in video games and everything that you can imagine under the sun. And it's really whatever capabilities you're seeing the foundation model companies invest in, or even application layer companies invest in, the evals are upstream of all of that.

**Elad** (1:50)
And are you also helping companies outside of the core foundation models with a similar type of hiring, or is it mainly just focused on AM models right now?

**Brendan Foody** (1:57)
Yeah, so actually when we started the business, it was totally unrelated to human data. It was just that we saw that there were phenomenally talented people all around the world that weren't getting opportunities, and we could apply LMs to make that process of finding them jobs more efficient. And then we realized after meeting a couple of customers in the market that there was just this huge vacuum because of the transition in the human data market, and that the human data market used to be this crowdsourcing problem of how do you get a bunch of low and medium skilled people that are writing barely grammatically correct sentences for the early versions of ChatGBT, and it was transitioning towards this vetting problem of how do you find some of the most capable people in the world that can work directly with researchers to push the frontier of model capabilities. But we've still kept that core DNA of hiring people for roles, human data and otherwise, and a lot of our customers hire for both.

**Elad** (2:52)
Do you think all of hiring eventually moves to these AI systems assessing people, or at least all sort of knowledge work?

**Brendan Foody** (2:57)
I think certainly, because we're already seeing on most of our evals that models are better than human hiring managers assessing talent, and it's still like the very early innings. And so I think we'll get to a point where we'll almost be irrational to not listen to the model, right, where people trust the model's recommendation. And like maybe for legal reasons, we'll still have the human pressing the button and making the final sign off. But where we just trust the model's recommendations on who should be doing a given task or job more than we trust the humans.

**Elad** (3:30)
I guess in any field people say that there's 10x people, there's 10x coders who are way more productive than the average coder, there's 10x physicians or investors or you name it. Do you see that in terms of the output of your models? In other words, are you able to identify people who are outliers?

**Brendan Foody** (3:44)
Totally. This is one of the most fascinating things is that the power law nature of knowledge work frames the importance of performance prediction. And imagine if you can understand the kinds of engineers on an engineering team that are going to perform in the 90th percentile. Or even if you could say, I know that this person that costs half as much is going to perform in the top quartile. It frames how you think about the value that we create for customers and how you think about the long-term economics of the business. And it all ties back to how do you measure the customer outcomes and really go on them.