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No PriorsNo Priors Ep. 110 | With Mercor CEO and Co-Founder Brendan Foody
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At a glance
WHAT IT’S REALLY ABOUT
AI Evals, Labor Displacement, and Building a Global Talent Marketplace
- Mercor CEO Brendan Foody explains how the company uses large language models to evaluate and match talent, initially for AI data-labeling and now broadly for high-skill roles. He argues that models already outperform most human hiring managers on text-based assessments and that evals for economically valuable work—especially agentic, tool-using tasks—are becoming a core bottleneck. The conversation explores power-law performance in knowledge work, how reinforcement fine-tuning (RFT) enables data-efficient customization of agents, and why building evals may become the most common knowledge job. They also discuss rapid job displacement, the future mix of humans and AI agents in a unified global labor market, and what skills remain worth cultivating as AI advances.
IDEAS WORTH REMEMBERING
5 ideasAI models already outperform most human hiring managers on text-based assessments.
Mercor finds that LLMs can more accurately predict job performance from resumes, interviews, and written work than typical human managers, especially in high-volume, comparable roles, making it increasingly irrational not to rely on model recommendations.
The human data market has shifted from low-skill crowd work to elite eval creation.
Early AI training relied on cheap, low-skill annotation; now the frontier is recruiting highly capable domain experts who can design nuanced evals and work closely with researchers to push model capabilities via RL and RFT.
Evals for economically valuable, agentic work are the main bottleneck to automating knowledge jobs.
Most benchmarks test narrow, zero-shot questions, but real jobs involve coordination, tool use, and end-to-end outcomes; building rich, job-like evals—especially for agents—is essential to turn existing model capability into automation.
Verifiable domains (math, code, clearly gradable tasks) will be automated fastest.
Where outputs can be checked automatically or against clear criteria, models can rapidly improve through reinforcement learning; harder domains have sparse, noisy signals (e.g., founder judgment, taste, persuasion) and will lag.
Labor displacement in many white-collar roles is likely to be fast and politically painful.
Foody anticipates significant near-term disruption in functions like customer support and recruiting, with broader sectors following; he expects populist backlash and a need to rethink wealth redistribution and human roles.
WORDS WORTH SAVING
5 quotesWe train models that predict how well someone will perform on a job better than a human can.
— Brendan Foody
For everything that we want LLMs to be good at, we need evals for those things.
— Brendan Foody
I think displacement in a lot of roles is going to happen very quickly and it's going to be very painful.
— Brendan Foody
It would not surprise me if [creating evals] becomes the most common knowledge work job in the world.
— Brendan Foody
It makes way for a global unified labor market that every candidate applies to and every company hires from.
— Brendan Foody
High quality AI-generated summary created from speaker-labeled transcript.
