Perspectives on real-world training data, commercial environments, and the infrastructure that separates winning models from the field.
Every robotics team we talk to has the same question buried in their due diligence: where did this footage come from, and can we actually use it? The answer matters more than you think.
Read more →We talk to physical AI teams every week. Here's what they're actually asking for — and why most of the data market is completely failing to deliver it.
Read more →Gig data looks cheaper on a spreadsheet. It costs more on a model. The difference between data captured by an employee doing their actual job versus a contractor pretending to do a job is not subtle.
Read more →We built FieldMesh because the alternative was watching the physical AI industry train on fake data and wonder why their robots kept failing in the real world.
Read more →Egocentric video sounds simple until you try to collect it at scale, in real commercial environments, with employed workers, under a data rights agreement that actually holds up.
Read more →Billions are flowing into humanoid robotics and physical AI. Almost none of it is going toward solving the actual bottleneck: the data these models need doesn't exist in any form you can buy.
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