Introduction — from datasets to demonstrations

When the early wave of machine learning focused on images and text, scaling was a matter of scraping the web and hiring small armies of annotators to label data. For behemoths of natural language and vision this proved sufficient: more data, more compute, bigger models. But building machines that live in the physical world — humanoid robots that reach, grasp, navigate and communicate — demands a different currency. These systems learn from demonstrations, interactions and the messy choreography of bodies in space. That means one thing, above all: human time.

Across China, new recruiting drives have emerged to meet that need. A growing economy of “cyber laborers” — gig workers, teleoperators, annotators and motion demonstrators — is helping train embodied AI at scale. They are the hands, eyes and decision loops that transform abstract models into machines that move with purpose.

What cyber laborers actually do

The work is varied but centers on creating high-quality, situated interaction data. Typical tasks include:

• Teleoperation and motion demonstration: Workers remote-control telepresence systems or human-shaped manipulators to perform tasks — picking up a cup, opening a door, making a gesture — generating direct control traces for imitation learning.
• Fine-grained annotation: Labeling affordances, object states, human intents and multi-step task sequences inside video streams so robots can learn not just what to do, but why and when.
• Interactive training: Engaging with robots in closed loops to correct behavior, disambiguate failures, and teach fallback strategies through trial-and-error guided by humans.
• Virtual environment curation: Creating and validating synthetic scenarios used to augment physical demonstrations — arranging objects, scripting behaviors, and vetting simulation fidelity.

These activities are often woven into platforms and gig marketplaces. Recruitment happens through social apps, online job boards and specialized labor brokers. The workforce spans students, laid-off workers, rural-to-urban migrants and tech-savvy gig workers who learn the tasks quickly and deliver repeatable demonstrations.

Why this matters: the embodied-AI bottleneck

Embodied systems are data-hungry in a way that differs qualitatively from text- or image-based AI. They need:

• Temporal sequences of actions tied to physical outcomes;
• Context-rich annotations: what the human intended, what the object afforded, how the environment changed;
• High-quality teleoperation recordings that reflect real human strategy and adaptability.

Automated simulation and synthetic data help, but they cannot fully replace real human demonstrations — not yet. That gap has produced a labor bottleneck. Models stall not for lack of compute but for lack of diverse, situated human guidance. Recruiting and coordinating large groups of cyber laborers is a pragmatic way to unlock progress.

Scale, speed and industrial design

What differentiates this movement from earlier annotation economies is scale and integration. Training pipelines are being redesigned to incorporate human-in-the-loop cycles at every stage of development: from rapid prototyping of robot behaviors to continuous deployment where humans patch failures in real time. This produces a feedback-rich environment where each human interaction directly accelerates iteration.

Beyond tech startups, manufacturing partners and platform operators, the work is increasingly treated as an industrial process. Dedicated spaces — labs, warehouses and training centers — are organized to maximize throughput. Workers are trained to produce consistent demonstrations, and quality control systems flag anomalies for retakes. The result is an assembly line for embodied intelligence.

Human labor, new economies

For many cyber laborers, the jobs represent new income streams and skill accumulation. Teleoperation demands dexterity, pattern recognition and rapid decision-making; annotating complex interactions builds a literacy in human-robot choreography. Some workers view these roles as temporary gigs, others as pathways into more stable tech work. Upskilling is real for some, but the economics are uneven. Compensation is frequently tied to task throughput on platforms, which can drive pressure for speed over depth.

The implications extend beyond wages. As human labor becomes embedded in AI pipelines, societies must confront questions about labor standards, collective bargaining, platform governance and the right to understand how one’s work shapes autonomous behavior. This is labor in the service of creating agents — a new intersection where temporary gigs affect long-term technological trajectories.

Risks: biases, safety and accountability

Human-generated training data is never neutral. The choices cyber laborers make — how they pick up objects, how they annotate intentions, which scenarios they prioritize — will be encoded in the resulting behavior of robots. If the workforce lacks demographic and geographic diversity, robots may learn a narrow set of physical habits and social norms.

There are also safety implications. Rapid human-in-the-loop cycles can mask brittle behaviors if oversight is incomplete. Robots trained primarily by a particular cohort may perform unexpectedly in new settings, with real physical consequences. Accountability becomes diffuse: who is responsible when a robot’s failure stems from a training shortcut produced by a gig task?

Governance and transparency

Scaling embodied AI through human work calls for governance mechanisms that balance innovation with worker rights and public safety. Possible measures include:

• Transparent dataset documentation that describes how demonstrations were collected and by whom, without exposing individuals to harm;
• Fair-pay standards and minimum task time requirements to discourage speed-driven shortcuts;
• Audit trails that link robot behaviors back to training inputs, enabling incident analysis and remediation;
• Cross-sector collaboration to establish safety benchmarks for embodied systems deployed in public spaces.

These are not easy prescriptions, but they are practical ones. They would help ensure that the machines shaped by human labor do not inherit the worst parts of the systems that trained them.

A geopolitical dimension

Embodied AI is a domain where hardware, software and human labor converge — and where industrial strategy matters. Countries and companies that can mobilize synchronized pipelines of data, compute and labor will move faster. The mass recruitment of cyber laborers in China is part of a global picture in which different nations pursue different emphases: some prioritize simulated scale, others the messy fidelity of human demonstrations.

The outcome will influence the pace at which humanoid robots move from research labs to factories, care settings and public environments. That matters not just economically but strategically, because physical-capable agents introduce new dimensions of influence and risk.

Two futures: uplift or extraction

There are two divergent trajectories ahead. In one, the rise of cyber laborers yields broad opportunities: new vocational tracks, democratized access to robot-building skills, and devices that better serve diverse human needs because they were trained by many hands. In the other, the model becomes an extractive gig economy that accelerates capability without protecting the people who enabled it. Robots learn to act at scale; humans who taught them remain precarious.

The difference will depend on choices made today: how platforms design incentives, how employers treat workers, and how governments and communities demand transparency and fairness.

What the AI news community should watch

For those covering this space, there are several things to track closely:

1. How platforms structure pay and quality control for embodied-data tasks;
2. Who is doing the teleoperation and how representative that workforce is of intended robot users;
3. Whether documentation standards for embodied datasets emerge and how complete they are;
4. How safety incidents tied to training data are investigated and remedied;
5. Policy responses at city, national and international levels that address worker protections and accountability for physical AI.

These are not peripheral details; they are core to the ways robots will behave and the societies that will host them.