After her second child was born, Chelsea Becker took an unpaid, year of leave from her full-time job as a flight attendant. After watching a video on TikTok, she found a side hustle: training artificial intelligence models for a website called Data Annotation Tech.

For a few hours each day, Becker, 33, who lives in Schwenksville, Pennsylvania, would sit at her laptop and interact with an AI-powered chatbot. For every hour of work, she was paid $20 to $40. From December to March, she made more than $10,000.

Chelsea Becker, who started training chatbots from home after taking an unpaid leave from her flight attendant job when her daughter was born, at home with her children in Schwenksville, Pa.Credit: New York Times

The boom in AI technology has put a more sophisticated spin on a kind of gig work that doesn’t require leaving the house. The growth of large language models such as the technology powering OpenAI’s ChatGPT has fuelled the need for trainers like Becker, fluent English speakers who can produce quality writing.

It is not a secret that AI models learn from humans. For years, makers of AI systems such as Google and OpenAI have relied on low-paid workers, typically contractors employed through other companies, to help computers visually identify subjects. They might label vehicles and pedestrians for self-driving cars or identify images on photos used to train AI systems.

But as AI technology has become more sophisticated, so has the job of people who must painstakingly teach it. Yesterday’s photo tagger is today’s essay writer.

There are usually two types of work for these trainers: supervised learning, where the AI learns from human-generated writing, and reinforcement learning from human feedback, where the chatbot learns from how humans rate their responses.

It’s fundamentally not a good idea to outsource or crowdsource concerns about safety and ethics.

Companies that specialise in data curation, including the San Francisco-based startups Scale AI and Surge AI, hire contractors and sell their training data to bigger developers. Developers of AI models, such as the Toronto-based startup Cohere, also recruit in-house data annotators.

It is difficult to estimate the total number of these gig workers, researchers said. But Scale AI, which hires contractors through its subsidiaries, Remotasks and Outlier, said it was common to see tens of thousands of people working on the platform at a given time.