Are you interested in getting paid $225 an hour for acting as a customer service agent in fluent Hebrew?
Too late. That gig is almost certainly gone now, along with the consulting opening for “a physician with more than three years of experience in the Rwandan primary care medical system.”
The help-train-your-own-replacement industry — also known as the training data collection industry — comes across like a swarm of locusts in today’s New York Times profile of some of the startups behind it. But the locusts have increasingly picky palates.
The piece works well as a companion to Ruth Fowler’s excellent write-up for WIRED two months ago, which we recommended at the time. Fowler described being one of many highly educated people competing for lucrative-if-unpredictable gig work — labeling videos, scoring outputs, answering tricky questions — until the boom times went bust, the work dried up, and the companies became increasingly exploitative of those who remained.
The new story in the Times, by Lora Kelley, visits Mercor, one of the companies being sued by the dangerously clever workers in Fowler’s piece over worker abuses and leaks of private data.
The generally well-educated are no longer as useful to these companies because the data in demand is so highly rarefied: Think “Ph.D. physicist with a specialization in general relativity, astrophysics or cosmology,” to quote the description of one listing. Mercor is actually trying to move even further up the value chain, aiming to “capture the output of entire companies.” Once companies are automatable they move on to others.
Automated companies? I coincidentally ran into this thread on X this morning with excerpts from a memo by the CEO of one of the Chinese AI companies: “We are already moving toward the fully automated, no-person company, or NPC,” he wrote. Maybe he should look at Argentina: We’ve previously covered how Argentina’s president is pitching his country as friendly to non-human corporations.
Anyway, in the Times article, Dr. Amanda Brown, a biologist who played the gig-work game, saw working conditions deteriorate as the bar rose:
Part of the challenge, Dr. Brown said, was that over only a few months, she noticed rapid improvements that made it trickier to find things the A.I. models didn’t already know, which made her shifts that much more difficult.
Brown eventually took a teaching gig.
There’s an end-times feel to the whole piece. These gig workers and the companies hiring them all recognize that their work reflects a time of transition during which humans still have something to teach AIs. But the AIs are learning fast.
The analyses and opinions expressed on AI StopWatch reflect the views of the individual contributors and the sources they cover, and should not be taken as official positions of the Machine Intelligence Research Institute.