Collecting robot training data is dirty, unglamorous work. Some AI labs are already paying XDOF to do it
Key takeaways
- Two weeks ago, Open AI said it would relaunch the robotics program it shuttered in 2021 — the latest signal that the biggest AI labs are racing to teach machines to operate in the physical world.
- That gap is creating a new kind of infrastructure business.
- XDOF (pronounced ecks-doff ), emerging from stealth today, is betting that the next great bottleneck in AI isn t models or chips, but the data feedback loop needed to teach robots how to interact with the physical world.
Why this matters: a development in AI with implications for how people work, create, and decide.
Two weeks ago, Open AI said it would relaunch the robotics program it shuttered in 2021 — the latest signal that the biggest AI labs are racing to teach machines to operate in the physical world. But building capable robots requires something the AI industry doesn t yet have, which is the training data to match that used for language models.
That gap is creating a new kind of infrastructure business. Unlike LLMs that were trained on a vast sea of publicly available text, robots need data that captures physical interaction, and that kind of data barely exists. YouTube videos and footage captured by gig workers are low-fidelity and hard to reconcile with the physical world.
XDOF (pronounced ecks-doff ), emerging from stealth today, is betting that the next great bottleneck in AI isn t models or chips, but the data feedback loop needed to teach robots how to interact with the physical world.