A growing cohort of robotics startups is addressing the critical data scarcity limiting humanoid and warehouse automation by manufacturing high-fidelity physical training datasets. At a San Leandro, California facility, Encord is pioneering this shift, moving beyond traditional data management to actively produce the real-world physical manipulation examples required to train next-generation AI models. The fundamental challenge mirrors early large language model development, where success depended on vast, readily available text corpora. Physical AI lacks an equivalent foundation. Training neural networks for precise manipulation demands datasets estimated to be five times the size of major video archives, a scale that forces companies to invest heavily in data generation rather than passive collection. Encord, founded to serve machine vision and robotics firms, has responded by building an internal data-creation team to manufacture these missing examples. Inside the San Leandro warehouse, robotic trainers execute complex manipulation tasks while wearing specialized sensor arrays. These pilots operate leader-follower robotic rigs, mimicking human movements for tasks ranging from stacking objects and pouring liquids to handling network cables. To enhance the dataset, Encord is trialing new sensor modalities developed by Zander Labs. The German neuroscience startup provides headsets that record brainwave activity, allowing researchers to map mental states such as error detection, intent, and surprise directly onto physical training sequences. According to Zander neuroscientist Lucas Gehrke, analyzing the cognitive load required for specific tasks will help robotics engineers determine when to deploy computationally intensive models. Complementing neuroscience data, Encord is deploying forearm-mounted electromyography sensors to capture muscle electrical signals. This approach addresses a common limitation in video-based training, where hands frequently leave the camera frame. By correlating arm signals with visual data, the system can reconstruct precise three-dimensional hand poses, providing more robust spatial understanding for robotic manipulation models. Every recorded sequence undergoes dense, physical annotation, describing exact actions such as grip pressure and joint movement. Encord executives estimate that this level of annotation multiplies dataset value by a factor of one hundred relative to standard egocentric video, despite requiring more resources to produce. The economics of physical AI data generation present a distinct hurdle compared to internet-scale text scraping. Manufacturing high-quality manipulation data requires specialized labor, hardware, and rigorous quality control, fundamentally altering the cost structure of robot learning. Nevertheless, industry leaders view this investment as necessary to overcome the manipulation fidelity gap. Encord positions its San Leandro facility as an industry clearinghouse, observing which data collection techniques gain traction across competing robotics programs. This cross-industry visibility allows the company to refine its offerings while accelerating the broader adoption of end-to-end learning in warehouse and household automation. As robotics companies transition from experimental research to scalable deployment, the ability to systematically generate, annotate, and distribute physical training data will dictate the pace of progress. Encord integration of neurophysiological signals, augmented motion capture, and precise annotation represents a strategic pivot in the physical AI landscape. By manufacturing the raw material that neural networks require, the company aims to transform data scarcity from a developmental bottleneck into a standardized industrial process.