The company reached that valuation with its $750 million Series D funding round, announced Tuesday (Nov. 10). Mercor, originally an AI-powered hiring platform, says it plans to use the financing to help train and test AI agents.
“AI is only as useful as the human expertise behind it, and as models grow more capable, the demand for specialized, domain-specific knowledge compounds,” Brendan Foody, Mercor’s founder and CEO, wrote on the company blog.
Foody argued that “every job in the economy” will need its own reliable AI agent, and that the company has spent the last two years developing infrastructure to train and evaluate frontier models on real-world work using human intelligence.
“We’re now bringing those same capabilities to enterprises across the economy to evaluate what those agents can do, what they cost, and how to improve their performance,” he wrote.
The CEO said Mercor is seeing the impact of AI investment on its own business, as it spends three times as much on inference as on employee salaries.
“More people will work alongside AI agents that can perform increasingly complex tasks, creating new opportunities for companies to apply human expertise at scale,” he said. “We’re organizing the human intelligence required to help enterprises measure, train, and improve those systems as they become part of the entire economy.”
Research by PYMNTS Intelligence has examined the ongoing economic impact of AI. According to the recent Agentic Edge Index report, some 64% of AI leaders describe their AI returns so far as very positive, though 66% expect full returns to take at least three years.
Executives also expect AI to expand what their workers can accomplish, with 84% expecting business growth without a matching increase in staff in the next three years, and three-quarters anticipating changes in employee roles and required skills.
“The results show plenty of room to advance. Nine in 10 enterprises score less than 25 on the index’s 100-point scale. Even the companies furthest ahead haven’t reached widespread independent execution,” PYMNTS wrote. “Getting there takes more than a technology purchase. Just more than half of executives say people, processes and company culture constrain AI more than the technology itself. Training and clear responsibilities deserve a place in the investment plan.”
