@Figure_robot just stacked the two halves of the Physical AI bottleneck a week apart. First Index: a creator network collecting real-world task video at roughly half an hour of uploads every second, across homes and workplaces in 100+ countries. That is the long-tail data you do not get from a lab teleop setup. Then Nscale: $3.5B of compute to start, intent past $6B, up to 100,000 NVIDIA Vera Rubin GPUs. @adcock_brett said they are largely constrained by data and compute. Index is the data bet. Nscale is the compute bet. Figure's own writeup says the quiet part: data alone cannot solve this. My take: Compute is a prerequisite. Collecting more data is critical. But finding the 1% of data that actually teaches the model is what compounds. Search and curation, not just capture. That's the problem we are focused on solving at @Foxglove.