The Human Touch in the Age of Humanoids: Gig Workers Fuel AI Training Boom
The race to build functional humanoid robots is on, and a surprising new workforce is powering the effort: gig workers recording everyday tasks in their homes. Individuals like Zeus are now “data recorders” for companies like Micro1, capturing video of themselves performing chores – ironing, wiping tables, opening microwaves – to train the next generation of robots. This burgeoning “chore content” economy is rapidly expanding, with robotics firms investing over $6 billion in humanoid development in 2025 alone.
Micro1, founded by 24-year-old Ali Ansari, has become a central player in this space, recruiting thousands of workers across more than 50 countries, including India, Nigeria, and Argentina. The company’s valuation skyrocketed from $80 million to $2.5 billion in just eight months after pivoting to focus on data labeling. While the work offers locally competitive pay, it also raises critical questions about data privacy and informed consent. Workers are vetted using an AI agent named Zara and must adhere to strict guidelines – keeping hands visible and maintaining a natural pace – to ensure data quality.
This reliance on human-demonstrated data stems from a fundamental challenge in AI development: robots lack the vast datasets that fueled the initial training of large language models. Unlike LLMs with access to the entire internet, robots must learn from the ground up through embodied AI, requiring extensive examples of tasks, teleoperation trajectories, and detailed annotations. Micro1 provides precisely this – structured recordings, robot teleoperation data, and fine-grained action segmentation with natural language descriptions. This data is then used for vision-language-action model training, imitation learning, and the creation of environment-specific datasets. Companies like Tesla, Figure AI, and Agility Robotics are among those seeking to leverage this approach.
Context: The Need for Real-World Data
Humanoid robots are designed to operate in complex, unstructured environments – homes, factories, warehouses – unlike the controlled settings of traditional industrial automation. This requires AI models trained on diverse, real-world data to handle the inherent variability and unpredictability of these spaces. Simply put, a robot trained only on simulated environments will struggle to adapt to the nuances of a human home.
The demand for this type of data is increasing rapidly, with robotics companies now spending over $100 million annually to acquire it. However, the approach is still in its infancy, and the optimal characteristics of effective training data remain unclear. Ansari emphasizes the need for “lots and lots of variations” to enable robots to generalize effectively, but workers report challenges in creating this diversity within the constraints of their living spaces.
This push for more realistic AI training coincides with a broader reassessment of how AI performance is measured. Current benchmarks often focus on isolated tasks, failing to reflect the messy, collaborative environments where AI actually operates. Researchers are advocating for new benchmarks that evaluate AI’s performance over longer time horizons, within human teams, and workflows. A recent proposal suggests a “Human–AI, Context-Specific Evaluation” approach to better assess AI’s real-world capabilities and risks.
As humanoid robots move closer to becoming a reality, the role of these often-invisible data recorders will likely become even more critical. But the long-term implications for these workers – and for the privacy of the data they generate – remain to be seen.
Given the rapid growth of this data-recording gig economy, how will companies ensure fair labor practices and protect the privacy of individuals contributing to the development of increasingly sophisticated AI systems?