The Verge – no paywall – You Could Be Next – The Verge and New York Magazine. “The LinkedIn post seemed like yet another scam job offer, but Katya was desperate enough to click. After college, she’d struggled to make a living as a freelance journalist, gone to grad school, then pivoted to what she hoped would be a more stable career in content marketing — only to find AI had automated much of the work. This company was called Crossing Hurdles, and it promised copywriting jobs starting at $45 per hour…

• Machine-learning systems learn by finding patterns in enormous quantities of data, but first that data has to be sorted, labeled, and produced by people. ChatGPT got its startling fluency from thousands of humans hired by companies such as Scale AI and Surge AI to write examples of things a helpful chatbot assistant would say and to grade its best responses. A little over a year ago, concerns began to mount in the industry about a plateau in the technology’s progress. Training models based on this type of grading yielded chatbots that were very good at sounding smart but still too unreliable to be useful. The exception was software engineering, where the ability of models to automatically check whether bits of code worked — did the code compile, did it print HELLO WORLD — allowed them to trial-and-error their way to genuine competence. The problem was that few other human activities offer such unambiguous feedback. There are no objective tests for whether financial analysis or advertising copy is “good.” Undeterred, AI companies set out to make such tests, collectively paying billions of dollars to professionals of all types to write exacting and comprehensive criteria for a job well done. Mercor, the company Katya stumbled upon, was founded in 2023 by three then-19-year-olds from the Bay Area, Brendan Foody, Adarsh Hiremath, and Surya Midha, as a jobs platform that used AI interviews to match overseas engineers with tech companies. The company received so many inquiries from AI developers seeking professionals to produce training data that it decided to adapt. Last year, Mercor was valued at $10 billion, making its trio of founders the world’s youngest self-made billionaires. OpenAI has been a client; so has Anthropic.
• Each of these data companies touts its stable of pedigreed experts. Mercor says around 30,000 professionals work on its platform each week, while Scale AI claims to have more than 700,000 “M.A.’s, Ph.D.’s, and college graduates.” Surge AI advertises its Supreme Court litigators, McKinsey principals, and platinum recording artists. These companies are hiring people with experience in law, finance, and coding, all areas where AI is making rapid inroads. But they’re also hiring people to produce data for practically any job you can imagine. Job listings seek chefs, management consultants, wildlife-conservation scientists, archivists, private investigators, police sergeants, reporters, teachers, and rental-counter clerks. One recent job ad called for experts in “North American early to mid-teen humor” who can, among other requirements, “explain humor using clear, logical language, including references to North American slang, trends, and social norms.” It is, as one industry veteran put it, the largest harvesting of human expertise ever attempted.
• These companies have found rich recruiting ground among the growing ranks of the highly educated and underemployed. Aside from the 2008 financial crash and the pandemic, hiring is at its lowest point in decades. This past August, the early-career job-search platform Handshake found that job postings on the site had declined more than 16 percent compared with the year before and that listings were receiving 26 percent more applications. Meanwhile, Handshake launched an initiative last year connecting job seekers with roles producing AI training data. “As AI reshapes the future of work,” the company wrote, announcing the program, “we have the responsibility to rethink, educate, and prepare our network to navigate careers and participate in the AI economy.”

There is an underlying tension between the predictions of generally intelligent systems that can replace much of human cognitive labor and the money AI labs are actually spending on data to automate one task at a time. It is the difference between a future of abrupt mass unemployment and something more subtle but potentially just as disruptive: a future in which a growing number of people find work teaching AI to do the work they once did. The first wave of these workers consists of software engineers, graphic designers, writers, and other professionals in fields where the new training techniques are proving effective. They find themselves in a surreal situation, competing for precarious gigs pantomiming the careers they’d hoped to have…”