Take this job ad seeking “professional translators” in Igbo, Nigeria that offers up to $17 an hour to help train generative AI models. That is well below the average rate for Nigerian translators, who tend to start at $25 an hour, according to Good Firms, a client-reviews website. The ad comes from Remotasks, the main platform of San Francisco-based AI startup Scale.ai, which just raised $1 billion from investors including Amazon.com Inc. in one of the year’s largest financing rounds. Scale.ai didn’t respond to multiple requests for comment.
The company and rivals like San Francisco-based Samasource Impact Sourcing Inc., Argentina’s Arbusta S.R.L. and Bulgaria’s Humans in the Loop play a critical role in the AI supply chain, but for years now have typically paid just enough for workers to maintain a living, Murgia and Dr. Miceli say.
That may continue even as data work becomes more complex. Recently, platforms like Scale.ai have been looking for more skilled workers, including artists and people with creative-writing degrees to write short stories for training AI systems, according to instruction documents seen by Miceli. While those offer higher wages, they are still below what people with degrees should be earning.
Researchers say the appetite for such work is growing, but with few incentives to provide an equitable wage, it’s hard to see workers’ economic status improving. Training AI is already horrifically expensive due to the cost of chips and cloud computing. (Venture capital firm Sequoia Capital recently calculated that the AI industry spent $50 billion on Nvidia Corp. chips to train AI in 2023 but only made about $3 billion in revenue.)
That spells fewer opportunities for the people underpinning the AI revolution and shows yet again that the technology’s true transformative effects have been in entrenching economic power.
Perhaps we can learn something from Nike Inc. Back in the 1990’s, the company faced an enormous backlash for the long hours and meager wages its workers in developing nations earned. Over time, consumer boycotts and pressure from the media led Nike to put in stricter labor policies. It spent millions of dollars on improving conditions and pay.
The challenge for data workers is that their jobs are harder to visualize in the same, concrete way you can imagine a young boy sewing tennis shoes in a dimly-lit warehouse, and that can make it harder for their advocates to rally support. But tech companies should remember that poor working conditions at the bottom of their supply chain can also lead to substandard AI. That’s problematic at a time when the public is more wary than ever of buzzy models that hallucinate. The answer to that isn’t rocket science: pay the data workers more and treat them better too.