How today’s AI was actually built
Modern AI models like ChatGPT and Claude were built in two stages. The first stage is pre-training: the model reads an enormous amount of text from the internet and learns to predict the next word, over and over, until it absorbs the patterns of language and a great deal of world knowledge. Pre-training makes a model fluent, but not yet genuinely helpful or reliable. The second stage is post-training. This is where humans step in. People are paid to show the model good answers, rank competing answers, and demonstrate exactly how a task should be done. The model learns from these human demonstrations and preferences. This is the stage that turned a raw text-predictor into something useful, and it is why an entire industry of companies (Scale AI, Surge AI, Mercor and others) now earns roughly ten billion dollars a year selling one thing to AI labs: high-quality human data.The one idea to hold onto. A model is only ever as good as the human data it learns
from. Clean, careful, well-labeled data produces a capable model. Sloppy or improperly
sourced data produces a weak model or a legal problem. That principle is the whole
reason opentez exists, and the reason your attention to detail is the product.
The next revolution: robots that learn by watching
Text was the easy part because it was already sitting on the internet. The next frontier is physical intelligence: robots that can fold laundry, tile a floor, frame a wall, or wire a panel. The models being built for this are called VLAs (Vision-Language-Action models). A VLA takes in what a camera sees (vision) and an instruction in plain language (“tile this section of floor”), and outputs the actual physical actions a robot should perform. Recent systems you may hear about include Google’s RT-2, OpenVLA, Physical Intelligence’s π-series, NVIDIA’s GR00T, and Figure’s Helix. They all work this way. Researchers describe this moment as robotics having its own “LLM moment.” But there’s a catch, and it’s the entire business opportunity: a robot cannot learn to tile a floor from Wikipedia. Language models had the whole internet to read. Robots have almost nothing. Physical skills live in people’s hands and habits, not in text. That data has to be captured deliberately by real people doing real work, and right now it barely exists.Egocentric data: the thing we collect
“Egocentric” simply means first-person point of view. Instead of filming a worker from across the room, the camera sits on the worker’s own head and sees what they see: their hands, their tools, the material, and where their attention goes as a task unfolds. This is exactly the viewpoint a robot needs, because it mirrors what the robot’s own camera will see when it tries to do the job. This isn’t theoretical. Meta’s Ego4D and Project Aria research programs proved that first-person video of everyday activity is gold for teaching machines, and university labs have shown that a person simply wearing a head camera and doing tasks produces training data comparable to far more expensive robot setups. opentez turns that proven idea into a real, paid, at-scale operation focused on skilled trades, the hands-on work robots most need to learn.What makes a clip valuable
| A valuable clip has… | Because… |
|---|---|
| Hands visible in frame, doing real work | Hands and tools are the single most important signal a robot learns from |
| A clear first-person angle from a head-mounted phone | It matches the viewpoint the robot will have |
| Continuous, focused activity | Robots learn the natural flow of a task, not idle time |
| Real, diverse trades and environments | Diversity is what lets a model generalize instead of memorizing |
| Clean provenance: consent on file, no minors or exposed PII | opentez only accepts data backed by a clear, documented chain of consent |
Where you fit
You are the source of the ground truth. Every robot trained on opentez data is, in a real sense, learning from you and your crew. Two roles make that possible:Recorders
Capture the footage while wearing the headband, doing the work, and uploading clean clips.
SPLs
Build and manage teams of recorders, guarantee the quality and legality of what comes
back, and deliver it to opentez in bulk.