Tekkl and his friend were recording conversations in Turkish about daily life to help train Elon Musk’s chatbot, Grok. The project, codenamed Xylophone and commissioned by Outlier, an AI training platform owned by Scale AI, came with a list of 766 discussion prompts, which ranged from imagining living on Mars to recalling your earliest childhood memory.
It was a job Tekkl had fallen into and come to love. Late last year, when depression and insomnia had stalled his art career, his older sister sent him a job posting she thought would be a perfect fit for the tech enthusiast and would help him pay for his rent and iced Americano obsession. On his best weeks, he earned about $1,500, which went a long way in Turkey. The remote work was flexible. And it let him play a small but vital role in the burgeoning world of generative AI.
Hundreds of millions of humans now use generative AI on a daily basis. Some are treating the bots they commune with as coworkers, therapists, friends, and even lovers. In large part, that’s because behind every shiny new AI model is an army of humans like Tekkl who are paid to train it to sound more human-like. Data labelers, as they’re known, spend hours reading a chatbot’s answers to test prompts and flag which ones are helpful, accurate, concise, and natural-sounding and which are wrong, rambling, robotic, or offensive. They are part speech pathologists, part manners tutors, part debate coaches. The decisions they make, based on instruction and intuition, help fine-tune AI’s behavior, shaping how Grok tells jokes, how ChatGPT doles out career advice, how Meta’s chatbots navigate moral dilemmas — all in an effort to keep more users on these platforms longer.
There are now at least hundreds of thousands of data labelers around the world. Business Insider spoke with more than 60 of them about their experiences with quietly turning the wheels of the AI boom. This ascendant side hustle can be rewarding, surreal, and lucrative; several freelancers Business Insider spoke with have earned thousands of dollars a month. It can also be monotonous, chaotic, capricious, and disturbing. Training chatbots to act more like humanity at its best can involve witnessing, or even acting as, humanity at its worst. Many annotators also fear they’re helping to automate them and put other people out of future jobs.
These are the secret lives of the humans giving voice to your chatbot.
Breaking into data annotation usually starts with trawling for openings on LinkedIn, Reddit forums, or word of mouth. To improve their chances, many apply to several platforms at once. Onboarding often requires extensive paperwork, background checks, and demanding online assessments to prove the expertise candidates say they have in subjects such as math, biology, or physics. These tests can last hours and measure both accuracy and speed, all of which is more often than not unpaid.
“I’m a donkey churning butter. And fine, that’s great. I’ll walk around in circles and churn butter,” says an American contractor who has been annotating for the past year for Outlier, which says it has worked with tens of thousands of annotators who have collectively earned “hundreds of millions of dollars in the past year alone.”
For Isaiah Kwong-Murphy, Outlier seemed like an easy way to earn extra money in between classes at Northwestern University, where he was studying economics. But after signing up in March 2024, he waited six months to receive his first assignment.
