Lawyers Bill $400 an Hour to Train AI Models

The latest:

Data work, long synonymous with cents-per-task microlabor, is now recruiting radiologists, physicists and civil rights attorneys at rates reaching $400 an hour, Bloomberg reported. Recruiters are canvassing LinkedIn for software engineers at up to $120 per hour and condensed matter experts at $170, all pitched as helping train next-generation AI systems. The platform Mercor says its average pay now stands at $110 per hour.

Details:

• The pay ladder: Bloomberg reported that recruiters advertise software engineers at up to $120 per hour, condensed matter experts at $170, and civil rights attorneys at $400, all framed as training next-generation AI systems. Mercor, which recruits white-collar data workers, put its average rate at $110 per hour but said it had no data on what share of tasks pay that much.
• The two tiers: The industry now splits between specialists it calls experts and lower-paid generalists handling less specialized tasks. Generalists told Bloomberg assignments are growing harder while steady work is scarcer. Prolific, a British platform, said experts make up 30% of its participants but declined to say how many of its tasks they complete.
• A worker’s decade: Brook Hansen began data work a decade ago transcribing audio and drawing boxes around people and cars on Amazon’s Mechanical Turk, earning a couple of hundred dollars a week. Hansen, an organizer with Turkopticon, said tasks later shifted to fact-checking documents and comparing chatbot answers, work demanding an hour of deep focus.
• The specialist case: Anu Brixey, a radiologist, earns in an hour what Hansen made in a week. Recruited four years ago to interpret chest, lung and heart scans, she signs up for at most two projects annually, marking features such as calcium in heart imaging, and told Bloomberg she does it for improved patient care rather than the money.
• Platform turnover: Mechanical Turk, once the most important platform for American data workers, shut down last month after 21 years, according to Bloomberg. Three-year-old Mercor has been discussing new funding at a $20 billion valuation. Micro1, another AI data startup, is valued at $4 billion.
• Why the shift: Publicly scraped internet material formed the foundation of today’s models, and companies are now racing to secure bespoke, privately held data to differentiate future versions. Former OpenAI engineer Will DePue argued in a blog post that data matters as much to the industry as computing power. Intermediaries are also paying for private phone chat logs and first-person chore videos.
• The skeptical read: Antonio Casilli, a professor at the Polytechnic Institute of Paris, assessed that highly paid experts join relatively few projects compared with generalists. He argued companies promising investors superintelligent machines prefer to appear to recruit superintelligent people while relying on the same workforce, calling the experts the cherry on top.
• The ownership problem: Danielle Li, an MIT professor studying AI and labor markets, noted that employers have always rented labor and lose access when they stop paying. She judged that dynamic breaks down once a company can own a version of that labor as a model, and that a small group of suppliers could substantially devalue an entire profession.
• Employers as suppliers: Micro1 founder Ali Ansari wrote in January that every hour of work can simultaneously run an organization, train AI models and generate extra revenue. Meta began installing software in April to track employee clicks, keystrokes and mouse movements, with an internal memo saying staff can improve its models simply by doing their daily work.
• Recruitment limits: Moonlighting is common among clinicians but harder for investment bankers, who are often bound by employment contracts and short on time, as Wall Street Oasis users told recruiters. Ana Price, a vice president at Prolific, said white-collar recruits are typically time-poor and money-rich, so compensation needs to be compelling.

Between the lines:

Two figures sit in tension: Mercor’s $110 average rate suggests specialists dominate its work, while Prolific’s refusal to break out expert task volume leaves the actual split unmeasured. That gap is exactly what Casilli’s skepticism targets. Meta’s keystroke tracking points to the endgame Ansari described, where firms buy data wholesale from employers rather than recruiting professionals one by one.

What’s next

Watch whether Mercor closes funding at the reported $20 billion valuation, whether platforms disclose what share of tasks specialists actually complete, and whether more employers follow Meta in converting routine staff activity into training data.