Companies like One X deploy robots that are remotely operated by humans to complete tasks. This strategy provides immediate value to customers while simultaneously collecting vast amounts of real-world training data, which is the primary bottleneck for developing full autonomy.

Unlike LLMs that train on the existing internet, robotics lacks a pre-training dataset for the physical world. This forces companies like Encore to build a full-stack solution combining a software platform for data management with human-led operations for data collection, annotation, and even real-time remote robot piloting for exception handling.

The business model for teleoperated robots like 1X's NEO isn't full autonomy but pairs a physical robot in a wealthy home with a human operator in another country. This creates a new form of globalized service labor, raising complex ethical questions about a future of "virtual housekeepers" and remote physical work.

While many in the robotics industry chase the "fully autonomous" narrative, teleoperation—having remote workers control machines with Xbox controllers—is an extremely valuable and practical step. Customers care about task completion, not the level of autonomy, making teleop a key tool for gathering training data and ensuring reliability.

An Indian company, Objectways, pays thousands of workers to wear headset cameras while performing manual tasks. This footage is sold as training data for humanoid robotics companies like Tesla's Optimus, effectively paying humans to accelerate their own obsolescence.

A user speculates on a future where you could buy a humanoid robot, get hired by the robot's manufacturer as a remote operator, and then get paid (with benefits) to teleoperate your own robot to do chores in your own house. This highlights a potential, albeit absurd, evolution of labor markets.

The labor force for teleoperated robots could be sourced from the gig economy. Ride-share drivers, for instance, could operate robots during their downtime between rides, creating a flexible, scalable, and cost-effective pool of on-demand human operators.

Progress in robotics for household tasks is limited by a scarcity of real-world training data, not mechanical engineering. Companies are now deploying capital-intensive "in-field" teams to collect multi-modal data from inside homes, capturing the complexity of mundane human activities to train more capable robots.

AI is creating a grim feedback loop where displaced white-collar workers are finding employment in data annotation. In these roles, they are paid to train the very AI systems that eliminated their previous, higher-skilled careers, perpetuating the cycle of automation.