becoming increasingly crypto x robotics pilled: - robots rely on vision models to interpret and navigate their environment. - they operate in a complex, multi-planar, three-dimensional world. - vision models require unique, real-world 3d datasets to enable physical movement and decision-making. - unlike llms that can be trained on the entire scrapable internet, there is currently no structured “real-world dataset” available to bootstrap novel vision model training runs. - heavy reliance on synthetic data in this space comes with significant disadvantages. - the “universe” of human-created tasks is effectively infinite, encompassing countless workflows, human mannerisms, and contextual nuances that need to be captured in trainable datasets. - even once real-world data is captured, structuring and labeling it remains highly challenging. crypto can provide incentives across the entire stack, from data collection to labeling, mobilizing large-scale, distributed human contribution.