Courtney Fahnhorst was looking for a way to supplement her income when she stumbled on a LinkedIn ad seeking medical experts to help train AI models.
The mother of four was already working a full-time job as a wound care and hyperbaric medicine specialist but wanted to earn some extra income to put toward a private practice she plans to open in St. Johns County, Florida, later this year.
“I really needed a job that was going to be flexible but also allowed me to build some income and capital to invest in my future,” she says. “This fit the bill perfectly.”
Over the last two years, the former ER doctor and wound care specialist has dedicated as much time as she can spare to sharing her 15 years of medical experience with the artificial intelligence software. “It’s posing a clinical situation, and I’m evaluating how well the model responds to it—where it gets it wrong, what misconceptions it may attract,” she says.
Though Fahnhorst was initially interested in the opportunity for the ability to set her own hours, she says she’s gotten a lot more out of the temporary contract position than just a paycheck.
“The salary is competitive with what I would make in a clinic-based or hospital-based environment, but it gives me flexibility. It really has also brought back that passion for medicine that I think a lot of us are missing,” she says.
“Getting back to the main reason behind what we do and taking away the bureaucratic things that tie our hands in medicine as physicians and providers—that really has been a very fun aspect that I was not expecting.”
Fahnhorst adds that training AI models for companies like her current employer, Mercor, also democratizes access to medical information.
“My patients go to Dr. Google, and have been for as long as I’ve been in medicine,” she says. “I do think there’s a great service in teaching these models correct information.”
Experts Wanted
Fahnhorst is just one of the 30,000 “experts” regularly imparting their professional knowledge to Mercor, which pays an average of $80 per hour, and about $3 million per day in total.
Mercor itself is just one of a growing number of companies exchanging career knowledge for cash, alongside Alignerr, Data Annotation, Outlier, Mindrift, RemoExperts, RWS, CrowdGen, and Handshake, to name a few.
“You can imagine how people would use ChatGPT or Gemini, and what they expect in terms of a response,” says Heidi Hagberg, Mercor’s head of communications. “In order for that response to be accurate and relevant, they need people with that expertise to deliver.”
Mercor doesn’t name its clients publicly. But Hagberg says the company works with “every single frontier AI company,” as well as a growing number of hypergrowth startups and Fortune 500 enterprises.
“We see this continuing to grow with our frontier labs but going across a lot of different sectors in the economy,” she says. “If you are a company that wants to build a custom agent to help respond to customer service tickets, or to help build market research agents, we are able to use our experts in our network to build those types of AI tools.”
Hagberg says so-called “experts” typically sign up with the platform for the ability to earn a flexible side income, for the opportunity to gain AI skills and work experience, and to have a direct hand in improving the software’s outputs.
“They work with us because they want to differentiate themselves and learn how to work alongside AI,” Hagberg says. “This is directly relevant to what they know and what they’re interested in. So being able to find $80-an-hour-work that they’re able to do on their own time is incredibly attractive.”
An Old Job on a New Level
The task of manually teaching technology isn’t new. But the size, scale and speed of the industry has exploded in the last couple of years alongside the growth of AI platforms.
“Data labeling at large has been around for over a decade, because it really started as drawing boxes around stop signs for autonomous vehicles, and very basic image recognition,” Hagberg says. “As AI evolved to the chatbots available to consumers today, that has expanded what it needs to be able to do. Millions of people are using them now, so you need to have a huge breadth of responses [to AI prompts].”
Now, the technology is evolving to require even more advanced domain expertise, as AI agents seek to complete multistep processes that utilize multiple platforms and services.
“We basically made mock software pieces—so like, mock Gmail or mock Workday or mock Excel—and then you have people perform multistep tasks that use those tools together,” says Handshake president Jonathan Stull.
“Let’s say you want to train an [AI] agent to help an accountant close their books. They’re not only doing that in QuickBooks, but it goes to get invoices from Gmail, and then looks at something in Slack, then brings it back to QuickBooks. If you want to train an agent to do multistep, multi-tool work, you actually have to track all those steps.”
It is the job of these AI trainers to walk through those steps to ultimately teach AI agents to complete them on their own.
An AI Jobs Boom?
Founded in 2014 as a platform to connect students and graduates with employers, Handshake added its AI division roughly 18 months ago in response to the growing demand it saw from employers on its hiring platform, Stull says. Since then, the company has paid out roughly $300 million to about 100,000 AI trainers, which it calls “fellows.”
“It’s a direct response to the demand,” Stull says. He explains that the skyrocketing valuations of AI companies like OpenAI and Anthropic demonstrate the level of competitiveness in the industry, adding that the models only advance when given more compute power and direct human insight.
As a career development platform that connected students and recent graduates with work opportunities, Handshake takes a slightly different approach to its AI training workforce, according to Stull, often framing the temporary contract roles within the context of its members’ career journey.
“Employers now want to hire more people with AI skills, AI fluency,” he says. “So if you can say, ‘I’m a PhD’ or ‘I’m an undergrad, and not only do I know economics—or music theory or software development—but I worked with a lot of foundational AI labs building out and training their data,’ that’s a huge advantage.”
Despite the industry’s recent rapid growth, insiders believe there’s still a long way to go before the need for AI trainers begins to slow. If it ever does.
“We believe—and I think most people in our space believe—this industry is going to double in the next year,” Stull says. “And then double again the next year after that.”
