How AI Labs Use Mercor to Obtain Data That Companies Won’t Share

Instead of signing expensive contracts with companies to get their data, AI labs these days are trying a new approach: tapping former senior employees at those companies for their industry knowledge, Mercor CEO Brendan Foudy said at TechCrunch Disrupt 2025 on Tuesday.

Speaking on stage, Foody described the Mercure marketplace as one of the main channels connecting former employees of investment banks, consulting houses and law firms with AI labs looking to automate those industries. Some of Mercor’s clients include OpenAI, Anthropic, and Meta.

“There’s an argument that Goldman Sachs doesn’t like the idea of ​​having models that are able to automate their value chain,” Foody said on stage, using the Wall Street giant as an example. “It definitely changes the competitive dynamics, and that’s part of the reason labs need us. Their customers don’t want to give them the data needed to automate large parts of their value chains, so they need to hire contractors who have previously worked at those companies, understand the workflow, and are willing to train models to automate them.”

Foody, the 22-year-old co-founder of Mercor, says his startup pays industry experts up to $200 an hour to fill out forms and write reports for AI training. The company now has tens of thousands of contractors, and says it distributes more than $1.5 million to them daily. However, Foody says the startup is still profitable because AI labs are willing to pay more for that valuable data.

In less than three years since its inception, Mercur has grown its annual recurring revenue to nearly $500 million, and recently raised $10 billion in funding.

Incumbents across the economy have good reasons to resist Mercur’s rise, as knowledge of their industry may leak out the back door through former employees in the startup’s market, which could eventually be used to automate their work. Foody acknowledged that it might expose inefficiency in the market, but said he would not call it a “loophole.”

In fact, Foody says some companies are already embracing this “new future of work.” He relished the idea that Mercur’s marketplace could create a new kind of gig economy, much as Uber did more than a decade ago. (Earlier this year, Sandeep Jain, Uber’s former chief product officer, joined Mercur as president.)

“There are companies that embrace this idea and realize that the world is going to change very quickly,” Foody said. “There is definitely another category of companies that are fearful, worried about disintermediation, and having their customers go straight to AI labs or application layer platforms. My hunch is that the first category will turn out to be on the right side of history.”

While Mercur tries to draw knowledge from various industries, Foody said his startup is trying to prevent contractors from committing corporate espionage — the illegal act of stealing proprietary information, trade secrets or intellectual property from one company and selling it to another.

But that is easier said than done. Most of Mercor’s workforce are former employees of law firms, investment banks and other industries that are highly confidential with their data. Foudy said some Mercur contractors were still working at their day jobs, just submitting data on the side, and claimed the contractors had been instructed not to upload documents from their former workplace. However, he acknowledged that “things could happen” given the size of his startup.

Foody argues that the knowledge in an employee’s head belongs to the employee, not to his or her company—a more generous view than many companies would accept. Additionally, in some of Mercor’s job ads, the startup draws the line between asking to know an employee and their company data.

For example, Mercor is currently looking for a CTO or startup co-founder who “can provide access to a large production database” for AI assessments, or potentially training AI models. In an email, Mercor told TechCrunch that a few technology executives at startups had taken up the offer, but declined to divulge details of their contracts.

Mercur was one of the first data startups to hire highly skilled knowledge workers in the United States and pay them large sums to train AI models. Early in the AI ​​boom, data vendors like Scale AI hired contractors in Third World countries to do fairly simple labeling tasks. Now, most of Mercor’s competitors — including Surge and Scale AI — have realized that AI labs need experts to improve their AI models. Many data vendors have also begun training “environments” to improve the ability of AI agents to complete real-world tasks.

Mercor clearly benefited from Scale AI’s misfortunes: several AI labs stopped working with Scale AI after Meta made a significant investment in the startup and hired its CEO. Last year, Merkur quintupled its value, but it is still smaller than Surge and Scale AI, which is valued at more than $20 billion.

Today, most of Merkur’s revenue comes from a few AI labs, but Foody says the startup plans to partner with other industries in the future. He believes companies in law, finance and medicine will need help leveraging their data to train AI agents – something Mercur specializes in.

“Over time, ChatGPT will be better than the best consulting firm, better than the best investment bank, better than the best law firm,” Foody said. “This will radically transform the economy, which will be a broadly positive force that helps create abundance for all.”