Mercor: The Young Innovators Transforming AI Recruitment and Data Labeling

In the heart of San Francisco’s bustling South of Market district, a trio of 22-year-old entrepreneurs is steering a company that has rapidly become one of the AI industry’s most notable success stories. Brendan Foody, Adarsh Hiremath, and Surya Midha—all Thiel Fellows who met on their high school debate team—founded Mercor in 2023, initially envisioning it as a next-generation recruitment platform that would use AI avatars to interview candidates and match them with companies. However, the timing of their launch coincided with the release of ChatGPT, which sparked an arms race among tech giants to develop increasingly sophisticated AI models. This technological gold rush created an unexpected opportunity: providing the human expertise needed to train these advanced AI systems. “It just doesn’t happen too often in startups where your biggest competitor gets torpedoed overnight,” Hiremath remarked, referring to the industry-shaking news in June when Meta purchased nearly half of data labeling giant Scale AI for $14 billion and recruited its CEO Alexandr Wang. This development created a significant opening for Mercor, as many AI labs began seeking alternative partners, concerned about Scale’s newfound ties to Meta potentially compromising its neutrality.

The company’s rapid ascent earned it the 89th spot on Forbes’ prestigious Cloud 100 list this year, and its growth metrics are impressive by any standard. By March, Mercor reported reaching $100 million in annualized revenue run rate and generated $6 million in profit during the first half of the year. The startup has maintained a remarkable growth pace, expanding by nearly 60% monthly over the past six months. This performance has attracted significant investment—$100 million in February from prominent backers including Felicis, Benchmark, and General Catalyst—catapulting its valuation to $2 billion, an eightfold increase from just months prior. The company’s distinguished investor roster includes Twitter co-founder Jack Dorsey, former Treasury Secretary Larry Summers, and tech kingmaker Peter Thiel. Benchmark partner Peter Fenton, an early investor in Twitter and Yelp who courted the founders with helicopter tours of San Francisco, describes them as “forces beyond categorization,” not merely prodigies.

What distinguishes Mercor in the increasingly crowded data labeling space is its focus on providing highly specialized experts—PhDs, lawyers, and other professionals who typically command between $90 and $150 hourly—to train the most sophisticated AI reasoning models. These experts are crucial for teaching AI systems how to “think” through complex, multi-step requests. Among the founders’ favorite staffing placements are a chess grandmaster and a private detective. Yash Patil, CEO of Applied Compute (a startup founded by former OpenAI employees), praises Mercor’s ability to “attract the caliber of talent that a lot of these other platforms aren’t able to.” This emphasis on quality over quantity has become Mercor’s key differentiator, with former Benchmark board member Victor Lazarte noting, “The people building models understand that the quality of your data is more important than the amount of data that you need.” Mercor’s sophisticated matching algorithms further enhance its value proposition by identifying the ideal expert for each specific project.

Despite its youth, Mercor is actively professionalizing its operations. Last month, the company hired former Uber product chief Sundeep Jain as its first president, bringing seasoned executive experience to complement the founders’ entrepreneurial energy. At 54, Jain jokes that he’s “significantly increasing the average age” at the company. His primary mission is scaling Mercor’s systems—from onboarding and management processes to creating better tracking and reporting mechanisms for customers. The company is also preparing to relocate to Instagram’s former offices in downtown San Francisco, securing larger space for its rapidly expanding team. This growth comes with challenges, particularly in an industry where Scale’s partial acquisition by Meta has raised questions about the long-term viability of data labeling as a business. However, Foody interprets this development differently, arguing that “the Meta deal validated the industry” by demonstrating that major tech companies find these services valuable enough to invest billions.

Mercor’s office culture reflects its youthful leadership and Silicon Valley heritage. The workspace features inspirational quotes from tech luminaries alongside those from Mercor’s own engineers, copies of Peter Thiel’s “Zero to One” scattered throughout, and an office bar cart that juxtaposes bottles of Dom Perignon with rows of Monster energy drinks. Foody, who grew up in Menlo Park with parents deeply embedded in the tech world (his mother worked for Meta’s real estate team while his father founded a graphics interface company), recalls valuable dinner table advice that shaped Mercor’s philosophy: create jobs and sell to “rich customers in pain.” He notes, “We joke that Mercor epitomizes both of those. We create jobs and we sell to the wealthiest customers.” The founders’ ambition has attracted not only substantial investment but also extravagant courtship from venture capitalists. Beyond Fenton’s helicopter tour, Felicis Ventures flew them to Las Vegas on a private jet to race Ferraris around the F1 track—an experience where Foody’s aggressive driving impressed Felicis partner Sundeep Peechu, who observed that “the appropriate risk-taker at the company should be the CEO.”

Looking forward, Mercor envisions expanding beyond AI model training to fulfill its original mission of revolutionizing recruitment across industries. The founders aim to eventually match professionals of all types—from lawyers to doctors—with appropriate positions, creating a comprehensive job-matching ecosystem. For now, though, the immediate opportunity lies in capitalizing on the void left by Scale’s new relationship with Meta. As AI development continues its rapid acceleration, the demand for high-quality human expertise to train these systems shows no signs of diminishing. “Human AI trainers will be necessary as long as there are complicated concepts to teach models,” Foody maintains, betting that this business will remain viable for the foreseeable future. With its combination of youth, technical acumen, and strategic vision, Mercor appears well-positioned to continue its remarkable trajectory in an industry that prizes both innovation and execution.