Feature Blog
Project Info
Everything that moves will eventually become autonomous. Robots come in many forms—every car today is already a robot. Now, we’re advancing toward building general-purpose robots. At their core, physical AI and robotics share the same foundational challenges: where do you get the data, what is the model architecture, and how do we scale?
We focus on the three essential pillars of the robotics industry: data, teleoperation, and models. These three function as a flywheel: large-scale data builds better foundation models, which boost tele-op efficiency; tele-op then fuels more real-world data collection, completing the loop.
Data at scale drives progress, but robotics is a data deficient space. The largest robotics datasets are only a few thousand hours of video, orders of magnitude smaller than the 15 trillion tokens used to train state of the art language models. At PrismaX, we’re building the protocols and mechanisms to validate and incentivize large-scale visual data, allowing robotics datasets to scale to the same heights as text data and enabling previously unattainable levels of accuracy and reliability.
Teleop improves performance and generates high quality data, but right now, every robotics company redoes the arduous work required to build a robust teleop stack. For many companies, managing hundreds of teleoperators in an unfamiliar country is a daunting or impossible task. We’re defining a uniform standard for teleop, providing turnkey access to operators, payments, and designs so robotics companies can focus on the things that make them different.
Models bring everything together. Visual data and teleop data complement each other during model training, the former providing diversity and scale during pretraining and the latter being critical during post-training and fine-tuning. Through collaboration with leading AI teams, we’re building models to power increasingly autonomous robots, amplifying the impact of our network by allowing operators to replace multiple physical workers, all while increasing the quality and quantity of the data we collect.
By tackling these core challenges, we advance the frontiers of robotics and bring the world towards an era where humans are empowered by machines to do more with less. We’re also establishing the new standard for fair use in the industry, one where the data that powers models generates revenue which is returned to the communities that create the data. Robotics is quickly approaching an inflection point, and backbone providers stand to win big.
