# AI can assess candidates while people focus on selling the role

Markdown copy of https://realalpha.app/s/ai-can-assess-candidates-while-people-focus-on-selling-the/ on Real Alpha, the best minutes from long interviews with the people building AI. Cite and link the page, not this copy.

- Speaker: [Brendan Foody](https://realalpha.app/builders/brendan-foody/) · Brendan Foody is co-founder and CEO of Mercor. · Co-founded Mercor, a labor marketplace supplying AI companies with expert data, paying tens of millions weekly.
- Host: Jordan Segall · Partner, Redpoint
- Show: Unsupervised Learning
- Episode: [Mercor CEO: Evals Will Replace Knowledge Work, AI x Hiring Today & the Future of Data Labeling](https://realalpha.app/v/SOZtz8IdI2w/), Jun 4, 2025
- Moment: 7:35 to 10:52 of the episode (3 min), playing on https://realalpha.app/s/ai-can-assess-candidates-while-people-focus-on-selling-the/; the episode from there: https://realalpha.app/v/SOZtz8IdI2w/?t=455

## Quote

> “And I think about it as enabling human recruiters and hiring managers to **spend all of their time** on the candidates they want to hire”
> — [Brendan Foody](https://realalpha.app/builders/brendan-foody/) at [8:18](https://realalpha.app/v/SOZtz8IdI2w/?t=498) · Unsupervised Learning

## Takeaways

1. AI can handle assessment while recruiters focus on selling the role.
2. Vary assessments or probe candidates' own work to reduce gaming.
3. Explainability helps customers trust decisions and check the reasons used.

## About this segment

Brendan Foody expects models to handle more candidate assessment, leaving recruiters to build candidate interest. Detailed, dynamic interviews are harder to game, and explanations help customers trust decisions.

From “Mercor CEO: Evals Will Replace Knowledge Work, AI x Hiring Today & the Future of Data Labeling”, Jun 4, 2025. [Watch the full episode](https://realalpha.app/v/SOZtz8IdI2w/)

## Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

[7:41](https://realalpha.app/v/SOZtz8IdI2w/?t=461) You were talking about the multimodal stuff, but I guess how do you see AI and human interviewers working together versus a world where it just kind of goes all AI assessment? Like at a simplistic level, the hiring process involves assessing candidates and selling candidates. And the assessment part, I think, is going to soon get so good from LLMs that it'll sort of be uh like foolish to think we know better right like will people just like take the recommendation because It'll like have proof that it is performing so much better on the eval, on the end outcome that customers care about, where humans, I think, will still continue to play a really large roles in the selling process of like this person that we're going to be working with and spending time with.

[8:24](https://realalpha.app/v/SOZtz8IdI2w/?t=504) And I think about it as enabling human recruiters and hiring managers to spend all of their time on the candidates they want to hire rather than all of these interviews of people that they don't end up wanting to hire. And so really, yeah, unlocking them to, you know, help people better understand the role, better understand the people that they're going to be working with and all the things that they should be excited about. Yeah, I love that. Will people start gaming the assessment? Is that something that you've seen at all? I guess the LLMs are picking up on certain things if you put in this keyword. They all decided they studied abroad. They all studied abroad in the place where they're recruiting for. Yeah, it's why sometimes you have to be a little bit secretive about the signals, right? But yeah, I mean, we have so many things where we deal with this,

[9:05](https://realalpha.app/v/SOZtz8IdI2w/?t=545) as every large hiring process does. And so I think the key is ensuring that assessments are relatively dynamic. Either like the problem that they're working on is change frequently or that you're asking them super in-depth questions about a particular part of their background. Because there's so much in the way of talent assessment that becomes possible when the models are able to do immense preparation for an interview, right?

[9:28](https://realalpha.app/v/SOZtz8IdI2w/?t=568) Like when I'm like doing a first interview of an executive candidate, like maybe sometimes I'll have references on them, but most of the time I look at their LinkedIn profile for a couple of minutes. I have like some preliminary notes, but imagine if I could go like listen to a podcast that they were on, right? Go read through like blog posts that they've written, all of the papers that they might've done during their PhD and ask about those things, right? You can get way more in depth and nuanced in a way that's very hard to game. Obviously you have these models that are pretty good at predicting how well these candidates will do. To me, it doesn't matter that that's explainable or like these models just have like, you know, in a black box, like, yeah, this person's going to be good and this person's not? Yeah, I think it does matter that it's explainable

[10:08](https://realalpha.app/v/SOZtz8IdI2w/?t=608) for sort of two reasons. First is for customers to like understand and trust those claims, right? Like building trust and all the reasoning chains. And then the second is obviously making sure that the models are selecting people for the right reasons, the reasons that they should be considering. And so it's beneficial, but I think like the end state of the economy is probably just that it'll be some sort of API or interaction where people want work done or they need some level of human involvement and just a confidence interval on how well that person will perform around the job.

[10:43](https://realalpha.app/v/SOZtz8IdI2w/?t=643) And there's far less of the intermediation that humans play in the process.