There is a vast hidden workforce behind AI

Mint

09-06-2025

WHEN DEEPSEEK, a hotshot Chinese firm, released its cheap large language model late last year it overturned long-standing assumptions about what it will take to build the next generation of artificial intelligence (AI). This will matter to whoever comes out on top in the epic global battle for AI supremacy. Developers are now reconsidering how much hardware, energy and data are needed. Yet another, less discussed, input in machine intelligence is in flux too: the workforce.
To the layman, AI is all robots, machines and models. It is a technology that kills jobs. In fact, there are millions of workers involved in producing AI models. Much of their work has involved tasks like tagging objects in images of roads in order to train self-driving cars and labelling words in the audio recordings used to train speech-recognition systems. Technically, annotators give data the contextual information computers need to work out the statistical associations between components of a dataset and their meaning to human beings. In fact, anyone who has completed a CAPTCHA test, selecting photos containing zebra crossings, may have inadvertently helped train an AI.
This is the 'unsexy" part of the industry, as Alex Wang, the boss of Scale AI, a data firm, puts it. Although Scale AI says most of its contributor work happens in America and Europe, across the industry much of the labour is outsourced to poor parts of the world, where lots of educated people are looking for work. The Chinese government has teamed up with tech companies, such as Alibaba and JD.com, to bring annotation jobs to far-flung parts of the country. In India the IT industry body, Nasscom, reckons annotation revenues could reach $7bn a year and employ 1m people there by 2030. That is significant, since India's entire IT industry is worth $254bn a year (including hardware) and employs 5.5m people.
Annotators have long been compared to parents, teaching models and helping them make sense of the world. But the latest models don't need their guidance in the same way. As the technology grows up, are its teachers becoming redundant?
Data annotation is not new. Fei Fei Li, an American computer scientist known as 'the godmother of AI", is credited with firing the industry's starting gun in the mid-2000s when she created ImageNet, the largest image dataset at the time. Ms Li realised that if she paid college students to categorise the images, which was then how most researchers did things, the task would take 90 years. Instead, she hired workers around the world using Mechanical Turk, an online gig-work platform run by Amazon. She got some 3.2m images organised into a dataset in two and a half years. Soon other AI labs were outsourcing annotation work this way, too.
Over time developers got fed up with the low-quality annotation done by untrained workers on gig-work sites. AI-data firms, such as Sama and iMerit, emerged. They hired workers across the poor world. Informal annotation work continued but specialist platforms emerged for AI work, like those run by Scale AI, which tests and trains workers. The World Bank reckons that between 4.4% and 12.4% of the global workforce is involved in gig work, including annotation for AI. Krystal Kauffman, a Michigan resident who has been doing data work online for a decade, reckons that tech companies have an interest in keeping this workforce hidden. 'They are selling magic—this idea that all these things happen by themselves," Ms Kauffman, says. 'Without the magic part of it, AI is just another product."
A debate in the industry has been about the treatment of the workers behind AI. Firms are reluctant to share information on wages. But American annotators generally consider $10-20 per hour to be decent pay on online platforms. Those in poor countries often get $4-8 per hour. Many must use monitoring tools that track their computer activity and are penalised for being slow. Scale AI has been hit with several lawsuits over its employment practices. The firm denies wrongdoing and says: 'We plan to defend ourselves vigorously."
The bigger issue, though, is that basic annotation work is drying up. In part, this was inevitable. If AI was once a toddler who needed a parent to point things out and to help it make sense of the world around it, the technology has grown into an adolescent who needs occasional specialist guidance and advice. AI labs increasingly use pre-labelled data from other AI labs, which use algorithms to apply labels to datasets.
Take the example of self-driving tractors developed by Blue River Technology, a subsidiary of John Deere, an agricultural-equipment giant. Three years ago the group's engineers in America would upload pictures of farmland into the cloud and provide iMerit staff in Hubli, India, with careful instructions on what to label: tractors, buildings, irrigation equipment. Now the developers use pre-labelled data. They still need iMerit staff to check that labelling and to deal with 'edge cases", for example where a dust cloud obscures part of the landscape or a tree throws shade over crops, confusing the model. A process that took months now takes weeks.
From baby steps
The most recent wave of AI models has changed data work more dramatically. Since 2022, when OpenAI first let the public play with its ChatGPT chatbot, there has been a rush of interest in large language models. Data from Pitchbook, a research firm, suggest that global venture-capital funding for AI startups jumped by more than 50% in 2024 to $131.5bn, even as funding for other startups fell. Much of it is going into newer techniques for developing AI, which do not need data annotated in the same way. Iva Gumnishka at Humans in the Loop, a social enterprise, says firms doing low-skilled annotation for older computer-vision and natural-language-processing clients are being 'left behind".
There is still demand for annotators, but their work has changed. As businesses start to deploy AI, they are building smaller specialised models and looking for highly educated annotators to help. It has become fairly common for adverts for annotation jobs to require a PhD or skills in coding and science. Now that researchers are trying to make AI more multilingual, demand for annotators who speak languages other than English is growing, too. Sushovan Das, a dentist working on medical-AI projects at iMerit, reckons that annotation work will never disappear. 'This world is constantly evolving," he says. 'So the AI needs to be improved time and again."
New roles for humans in training AI are emerging. Epoch AI, a research firm, reckons the stock of high-quality text available for training may be exhausted by 2026. Some AI labs are hiring people to write chunks of text and lines of code that models can be trained on. Others are buying synthetic data, created using computer algorithms, and hiring humans to verify it. 'Synthetic data still needs to be good data," says Wendy Gonzalez, the boss of Sama, which has operations east Africa.
The other role for workers is in evaluating the output from models and helping to hammer it into shape. That is what got ChatGPT to perform better than previous chatbots. Xiaote Zhu at Scale AI provides an example of the sort of open-ended tasks being done on the firm's Outlier platform, which was launched in 2023 to facilitate the training of AI by experts. Workers are presented with two responses from a chatbot recommending an itinerary for a holiday to the Maldives. They need to select which response they prefer, rate it, explain why the answer is good or bad and then rewrite the response to improve it.
Ms Zhu's example is a fairly anodyne one. Yet human feedback is also crucial to making sure AI is safe and ethical. In a document that was published after the launch of ChatGPT in 2022, OpenAI said it had hired experts to 'qualitatively probe, adversarially test and generally provide feedback" on its models. At the end of that process the model refused to respond to certain prompts, such as requests to write social-media content aimed at persuading people to join al-Qaeda, a terrorist group.
Flying the nest
If AI developers had their way they would not need this sort of human input at all. Studies suggest that as much as 80% of the time that goes into the development of AI is spent on data work. Naveen Rao at Databricks, an AI firm, says he would like models to teach themselves, just as he would like his own children to do. 'I want to build self-efficacious humans," he says. 'I want them to have their own curiosity and figure out how to solve problems. I don't want to spoon-feed them every step of the way."
There is a lot of excitement about unsupervised learning, which involves feeding models unlabelled data, and reinforcement learning, which uses trial and error to improve decision-making. AI firms, including Google DeepMind, have trained machines to win at games like Go and chess by playing millions of contests against themselves and tracking which strategies work, without any human input at all. But that self-taught approach doesn't work outside the realms of maths and science, at least for the moment.
Tech nerds everywhere have been blown away by how cheap and efficient DeepSeek's model is. But they are less impressed by DeepSeek's attempt to train AI using feedback generated by computers rather than humans. The model struggled to answer open-ended questions, producing gobbledygook in a mixture of languages. 'The difference is that with Go and chess the desired outcome is crystal clear: win the game," says Phelim Bradley, co-founder of Prolific, another AI-data firm. 'Large language models are more complex and far-reaching, so humans are going to remain in the loop for a long time."
Mr Bradley, like many techies, reckons that more people will need to get involved in training AI, not fewer. Diversity in the workforce matters. When ChatGPT was released a few years ago, people noticed that it overused the word 'delve". The word became seen as 'AI-ese", a telltale sign that the text was written by a bot. In fact, annotators in Africa had been hired to train the model and the word 'delve" is more commonly used in African English than it is in American or British English. In the same way as workers' skills and knowledge are transferred to models, their vocabulary is, too. As it turns out, it takes more than just a village to raise a child.
Clarification: This article has been amended to reflect Scale AI's claim that most of its labour is based in America and Europe.