First Block: Interview with Brendan Foody, Co-Founder and CEO of Mercor

Mercor’s co-founder and CEO Brendan Foody frames the company’s mission as an efficiency upgrade to hiring: automate repetitive recruiting steps while improving candidate availability, matching quality, and time-to-hire. The core idea is that “vibes-based” decisions can be reduced by grounding selection in performance data—predicting (1) whether a candidate will be interested in a role and (2) whether that candidate will perform well once hired. The result is a recruiting system designed to be more transparent and less biased, while still leaving room for human judgment at the final stage.

Foody ties Mercor’s approach to a long-running obsession with talent allocation, a problem he calls both universal—everyone spends their time through work—and surprisingly under-addressed by most companies. His path to recruiting software began with early hustles that taught him about incentives, competition, and scaling: selling donuts for profit in middle school, then undercutting a competitor’s higher-cost donuts, and later doing AWS consulting for classmates who were eligible for startup credits but weren’t applying. Those experiences evolved into building recruiting workflows with friends, hiring engineers internationally, and partnering with a code club at IIT Kharagpur to source talent. As ChatGPT gained traction, Foody says the manual resume reviews and interview processes his teams were using felt antiquated—and ripe for automation.

The company’s scale, as shared in the conversation, is striking. Mercor reportedly grew from a one-to-100 million revenue run rate in 11 months, reached well over half a million users, and has conducted hundreds of thousands of interviews across roles spanning software engineering, medicine, law, finance, and HR. Mercor also works with six of the Magnificent Seven and all of the top five AI labs, according to Foody. He credits this momentum to building the right product while the market shifted from crowdsourcing low- and medium-skilled work toward sourcing and vetting exceptional people to collaborate directly with AI researchers.

On hiring and leadership, Foody emphasizes a high bar early: keep standards as high as possible for the first 10 hires because those early decisions shape the next hundred. As the company grows past the “friend group” phase, culture must be operationalized through processes and values. Three values anchor Mercor’s culture: a can-do attitude that optimizes toward larger outcomes, high standards (especially in hiring), and intensity—working “like no one else.” He also describes a leadership scaling principle: build a network of people the CEO deeply trusts to quarterback initiatives to completion, and cultivate decision-makers who hold strong opinions loosely, updating quickly when evidence contradicts them.

Fundraising is treated less as a trophy and more as an input to building. Foody recounts bootstrapping to a million-dollar revenue run rate before dropping out of college, then raising a seed round with General Catalyst, followed by Series A and Series B. He highlights that investors moved quickly after seeing customer conversations and inflection metrics. Even with rapid growth, he warns that startups oscillate between extreme highs and lows, and recommends long-term perspective anchored in metrics and a north-star metric that compounds over a decade.

Finally, Foody’s product philosophy is practical: automate repetitive steps, improve matching by combining interest and performance prediction, reduce bias by focusing on interview transcripts rather than multimodal “fit,” and use humans for final judgment once the funnel is diversified. If Mercor were rebuilt today, he says the first block would still be the team—because execution depends on trusted people.