The Hidden Workforce

Big Tech has sold us the illusion that artificial intelligence is a frictionless technology — machines training machines, algorithms improving themselves, intelligence emerging from nowhere. But hidden beneath this smooth surface and vague metaphors of "the cloud" lies the grim reality of a precarious global workforce of millions labouring under often appalling conditions to make AI possible.

What Do AI Workers Do?

The workers powering AI fall into several categories, each performing tasks that machines cannot (yet) do for themselves.

The most widespread form of AI labour is data labelling and annotation. Before an AI can learn to recognise a stop sign, a cat, or a tumour, humans must label millions of images, identifying and tagging each object. This work includes categorising text, annotating conversations, classifying emotions, and identifying objects in video footage. It is tedious, repetitive work that requires concentration and accuracy, and it pays as little as one cent per task.

Content moderation represents some of the most psychologically demanding work in the AI supply chain. For AI systems to generate "safe" outputs, they must first learn what is unsafe. This means humans must review and label the most horrific content the internet has to offer. Workers describe reading hundreds of pieces of such content per day. The goal is to teach AI to filter this material out, but the human cost is severe.

Reinforcement Learning from Human Feedback (RLHF) is the technique that made ChatGPT possible. Human workers evaluate AI outputs, judging which responses are helpful, harmful, safe, or nonsensical. They teach models how to "sound human" and stay within moral boundaries. This work requires cultural knowledge, language fluency, and judgement — skills that command high wages in wealthy countries, but are paid at a fraction of that rate when outsourced.

Finally, synthetic dialogue creation involves workers writing thousands of sample conversations that AI can learn from, making them the uncredited ghostwriters behind every chatbot.

Where Are These Workers?

The AI labour supply chain follows familiar patterns of global exploitation. Companies like OpenAI, Meta, Google, and Microsoft contract with intermediary firms such as Sama, Scale AI, Majorel, and Teleperformance, which in turn hire workers in countries with lower wages and fewer labour protections.

Kenya has emerged as the most prominent hub for AI data work, with Nairobi hosting operations for Meta, OpenAI, TikTok, and numerous other companies. The Philippines, India, Venezuela, Uganda, Ghana, Rwanda, Nigeria, and Pakistan also serve as major hubs.

The wage disparity is staggering. US-based annotators might make $10–$25 an hour, while Kenyan annotators make as little as $1–$2 an hour for the same work.

Case Studies

OpenAI and Sama: The Workers Who Made ChatGPT "Safe"

The story that brought AI labour exploitation to global attention was TIME magazine's January 2023 investigation into OpenAI's use of Kenyan workers to make ChatGPT less toxic.

GPT-3 had a significant problem: it was prone to producing violent, sexist, and racist output. To address this, OpenAI needed to build a safety system. OpenAI contracted this work to Sama, a San Francisco-based company that employs workers in Kenya, Uganda, and India.

Beginning in November 2021, Sama workers in Nairobi were sent snippets of text describing the most horrific content imaginable. They were paid between $1.32 and $2 per hour to read this material, label it, and feed it back to OpenAI.

As one worker told TIME: "That was torture. You will read a number of statements like that all through the week. By the time it gets to Friday, you are disturbed from thinking through that picture."

Critically, OpenAI told reporters it believed it was paying Sama contractors $12.50 per hour. Workers say they actually received approximately $1–$2 per hour, sometimes less.

Scale AI and Remotasks: The Billionaire's Sweatshop

Scale AI, valued at $13.8 billion in 2024, has partnered with OpenAI, Meta, Google, Microsoft, and the US Department of Defense. The company operates through subsidiaries including Remotasks and Outlier.

The Fairwork project found Remotasks scored just 1 out of 10 on fair labour practices. In March 2024, Remotasks abruptly shut down operations in Kenya and Nigeria with minimal notice, stranding thousands of workers.

In January 2025, Scale AI was sued in US federal court by contractors alleging the company violated worker safety laws by exposing them to emotionally distressing content.

Meta, TikTok, and Content Moderation

Daniel Motaung, a South African who worked as a Facebook content moderator for Sama in Nairobi, became a key whistleblower. He described being exposed to graphic violence from his first day on the job. He developed severe PTSD. When he tried to organise his colleagues into a union, he was fired.

In May 2022, Motaung sued Meta and Sama in Kenyan courts. In September 2024, the Kenyan Court of Appeal ruled against Meta, allowing the case to proceed to trial. A 2024 report found that more than 140 of the Kenyan content moderators involved had been diagnosed with severe PTSD.

The Human Cost

Workers report a devastating range of mental health impacts including PTSD, depression, anxiety, panic attacks, and insomnia. A 2025 survey of 76 workers from Colombia, Ghana, and Kenya reported 60 independent incidents of psychological harm.

Workers are often bound by strict Non-Disclosure Agreements, legally prevented from speaking about what they see or how it affects them.

Fighting Back: Unions and Legal Action

On May 1, 2023 — International Workers' Day — more than 150 content moderators gathered in Nairobi and voted to establish the first African Content Moderators Union.

In April 2025, content moderators launched the first-ever Global Trade Union Alliance of Content Moderators, bringing together workers from nine countries to fight for living wages, safe working conditions, and union representation.

The alliance is demanding direct employment by tech companies rather than outsourced contracts; mental health support and safe working conditions; higher wages that reflect the skill and importance of the work; and the right to organise without retaliation.

What Has Changed Since 2023?

The human cost of AI is now part of public discourse. Worker organising has transformed the landscape. Legal precedents are being established. Yet despite increased attention, the fundamental business model hasn't changed. Companies continue to outsource AI labour to the cheapest markets. As AI systems become more sophisticated, they require more human labour to train and maintain, not less.

Teaching Human Labour in AI

  • English: Analyse how the language of "automation" and "intelligence" obscures human labour. Compare corporate press releases with investigative journalism.
  • Geography: Map how AI reproduces colonial patterns of extraction. Examine push and pull factors in AI labour outsourcing.
  • Legal Studies: Examine the Meta/Kenya lawsuits and jurisdictional questions for multinational accountability.
  • Business Studies: Analyse Scale AI's business model and the true cost of AI.
  • Psychology / Health: Research PTSD and secondary trauma in content moderation work.
  • Digital Technologies: Audit AI systems to identify where human labour is involved.
  • History: Draw parallels to textile industry exploitation and historical labour movements.
  • Civics: Examine what responsibilities consumers have for the conditions in which products are made.