HumanScale research demonstrates that egocentric human video pretraining produces better embodied AI performance than training on real robot data, with implications for scaling strategies across robotics labs. This reorders the cost structure of embodied AI development. Robot hardware acquisition and operation—historically the primary pretraining bottleneck—becomes optional for initial capability gains. Human video scales to orders of magnitude larger datasets at negligible marginal cost through existing internet archives and crowdsourced collection. Labs can defer expensive robotic infrastructure investment until task-specific finetuning, when domain adaptation becomes necessary. For builders, this shifts the pretraining workflow: prioritize human video dataset curation and filtering rather than expanding robot fleets. Operators managing embodied AI programs can reduce upfront capital expenditure on hardware while accelerating model iteration. The second-order effect accelerates transfer learning workflows—teams optimize for human-to-robot domain gaps during finetuning rather than addressing data scarcity during foundation training. This likely concentrates hardware investment at the finetuning and deployment stage rather than spreading it across pretraining.