Most of the work on Remotasks is paid at a piece rate with a single task earning anywhere from a few cents to several dollars. Because tasks can take seconds or hours, wages are hard to predict. When Remotasks first arrived in Kenya, annotators said it paid relatively well — averaging about $5 to $10 per hour depending on the task — but the amount fell as time went on.
Scale AI spokesperson Anna Franko said that the company’s economists analyze the specifics of a project, the skills required, the regional cost of living, and other factors “to ensure fair and competitive compensation.” Former Scale employees also said pay is determined through a surge-pricing-like mechanism that adjusts for how many annotators are available and how quickly the data is needed.
According to workers I spoke with and job listings, U.S.-based Remotasks annotators generally earn between $10 and $25 per hour, though some subject-matter experts can make more. By the beginning of this year, pay for the Kenyan annotators I spoke with had dropped to between $1 and $3 per hour.
That is, when they were making any money at all. The most common complaint about Remotasks work is its variability; it’s steady enough to be a full-time job for long stretches but too unpredictable to rely on. Annotators spend hours reading instructions and completing unpaid trainings only to do a dozen tasks and then have the project end. There might be nothing new for days, then, without warning, a totally different task appears and could last anywhere from a few hours to weeks. Any task could be their last, and they never know when the next one will come.
This boom-and-bust cycle results from the cadence of AI development, according to engineers and data vendors. Training a large model requires an enormous amount of annotation followed by more iterative updates, and engineers want it all as fast as possible so they can hit their target launch date. There may be monthslong demand for thousands of annotators, then for only a few hundred, then for a dozen specialists of a certain type, and then thousands again. “The question is, Who bears the cost for these fluctuations?” said Jindal of Partnership on AI. “Because right now, it’s the workers.”
“I really am wasting my life here if I made somebody a billionaire and I’m earning a couple of bucks a week.”
To succeed, annotators work together. When I told Victor, who started working for Remotasks while at university in Nairobi, about my struggles with the traffic-control-directors task, he told me everyone knew to stay away from that one: too tricky, bad pay, not worth it. Like a lot of annotators, Victor uses unofficial WhatsApp groups to spread the word when a good task drops. When he figures out a new one, he starts impromptu Google Meets to show others how it’s done. Anyone can join and work together for a time, sharing tips. “It’s a culture we have developed of helping each other because we know when on your own, you can’t know all the tricks,” he said.
Because work appears and vanishes without warning, taskers always need to be on alert. Victor has found that projects pop up very late at night, so he is in the habit of waking every three hours or so to check his queue. When a task is there, he’ll stay awake as long as he can to work. Once, he stayed up 36 hours straight labeling elbows and knees and heads in photographs of crowds — he has no idea why. Another time, he stayed up so long his mother asked him what was wrong with his eyes. He looked in the mirror to discover they were swollen.
Annotators generally know only that they are training AI for companies located vaguely elsewhere, but sometimes the veil of anonymity drops — instructions mentioning a brand or a chatbot say too much. “I read and I Googled and found I am working for a 25-year-old billionaire,” said one worker, who, when we spoke, was labeling the emotions of people calling to order Domino’s pizza. “I really am wasting my life here if I made somebody a billionaire and I’m earning a couple of bucks a week.”
Victor is a self-proclaimed “fanatic” about AI and started annotating because he wants to help bring about a fully automated post-work future. But earlier this year, someone dropped a Time story into one of his WhatsApp groups about workers training ChatGPT to recognize toxic content who were getting paid less than $2 an hour by the vendor Sama AI. “People were angry that these companies are so profitable but paying so poorly,” Victor said. He was unaware until I told him about Remotasks’ connection to Scale. Instructions for one of the tasks he worked on were nearly identical to those used by OpenAI, which meant he had likely been training ChatGPT as well, for approximately $3 per hour.
“I remember that someone posted that we will be remembered in the future,” he said. “And somebody else replied, ‘We are being treated worse than foot soldiers. We will be remembered nowhere in the future.’ I remember that very well. Nobody will recognize the work we did or the effort we put in.”
Identifying clothing and labeling customer-service conversations are just some of the annotation gigs available. Lately, the hottest on the market has been chatbot trainer. Because it demands specific areas of expertise or language fluency and wages are often adjusted regionally, this job tends to pay better. Certain types of specialist annotation can go for $50 or more per hour.
A woman I’ll call Anna was searching for a job in Texas when she stumbled across a generic listing for online work and applied. It was Remotasks, and after passing an introductory exam, she was brought into a Slack room of 1,500 people who were training a project code-named Dolphin, which she later discovered to be Google DeepMind’s chatbot, Sparrow, one of the many bots competing with ChatGPT. Her job is to talk with it all day. At about $14 an hour, plus bonuses for high productivity, “it definitely beats getting paid $10 an hour at the local Dollar General store,” she said.
Also, she enjoys it. She has discussed science-fiction novels, mathematical paradoxes, children’s riddles, and TV shows. Sometimes the bot’s responses make her laugh; other times, she runs out of things to talk about. “Some days, my brain is just like, I literally have no idea what on earth to ask it now,” she said. “So I have a little notebook, and I’ve written about two pages of things — I just Google interesting topics — so I think I’ll be good for seven hours today, but that’s not always the case.”
Each time Anna prompts Sparrow, it delivers two responses and she picks the best one, thereby creating something called “human-feedback data.” When ChatGPT debuted late last year, its impressively natural-seeming conversational style was credited to its having been trained on troves of internet data. But the language that fuels ChatGPT and its competitors is filtered through several rounds of human annotation. One group of contractors writes examples of how the engineers want the bot to behave, creating questions followed by correct answers, descriptions of computer programs followed by functional code, and requests for tips on committing crimes followed by polite refusals. After the model is trained on these examples, yet more contractors are brought in to prompt it and rank its responses. This is what Anna is doing with Sparrow. Exactly which criteria the raters are told to use varies — honesty, or helpfulness, or just personal preference. The point is that they are creating data on human taste, and once there’s enough of it, engineers can train a second model to mimic their preferences at scale, automating the ranking process and training their AI to act in ways humans approve of. The result is a remarkably human-seeming bot that mostly declines harmful requests and explains its AI nature with seeming self-awareness.
Put another way, ChatGPT seems so human because it was trained by an AI that was mimicking humans who were rating an AI that was mimicking humans who were pretending to be a better version of an AI that was trained on human writing.