Unlike LLMs that train on the existing internet, robotics lacks a pre-training dataset for the physical world. This forces companies like Encore to build a full-stack solution combining a software platform for data management with human-led operations for data collection, annotation, and even real-time remote robot piloting for exception handling.

To overcome the data bottleneck in robotics, Sunday developed gloves that capture human hand movements. This allows them to train their robot's manipulation skills without needing a physical robot for teleoperation. By separating data gathering (gloves) from execution (robot), they can scale their training dataset far more efficiently than competitors who rely on robot-in-the-loop data collection methods.

The rapid progress of many LLMs was possible because they could leverage the same massive public dataset: the internet. In robotics, no such public corpus of robot interaction data exists. This “data void” means progress is tied to a company's ability to generate its own proprietary data.

For consumer robotics, the biggest bottleneck is real-world data. By aggressively cutting costs to make robots affordable, companies can deploy more units faster. This generates a massive data advantage, creating a feedback loop that improves the product and widens the competitive moat.

Physical Intelligence demonstrated an emergent capability where its robotics model, after reaching a certain performance threshold, significantly improved by training on egocentric human video. This solves a major bottleneck by leveraging vast, existing video datasets instead of expensive, limited teleoperated data.

Progress in robotics for household tasks is limited by a scarcity of real-world training data, not mechanical engineering. Companies are now deploying capital-intensive "in-field" teams to collect multi-modal data from inside homes, capturing the complexity of mundane human activities to train more capable robots.

Previously, imitation learning required a single expert to collect perfectly consistent data, a major bottleneck. Diffusion models unlocked the ability to train on multi-modal data from various non-expert collectors, shifting the challenge from finding niche experts to building scalable data acquisition and processing systems.

The future of valuable AI lies not in models trained on the abundant public internet, but in those built on scarce, proprietary data. For fields like robotics and biology, this data doesn't exist to be scraped; it must be actively created, making the data generation process itself the key competitive moat.

The adoption of powerful AI architectures like transformers in robotics was bottlenecked by data quality, not algorithmic invention. Only after data collection methods improved to capture more dexterous, high-fidelity human actions did these advanced models become effective, reversing the typical 'algorithm-first' narrative of AI progress.