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.

According to Figure's CEO, the company's biggest challenge is no longer hardware reliability but acquiring enormous amounts of diverse, high-quality data. This data is essential for pre-training their Helix AI model to generalize and handle countless real-world scenarios in homes and commercial settings.

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.

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.

Ken Goldberg quantifies the challenge: the text data used to train LLMs would take a human 100,000 years to read. Equivalent data for robot manipulation (vision-to-control signals) doesn't exist online and must be generated from scratch, explaining the slower progress in physical AI.

According to Comma AI's CTO, the next frontier in robotics isn't just bigger models, but solving three fundamental challenges: 1) using ML for low-level controls, 2) making reinforcement learning (RL) practical for noisy environments, and 3) enabling continual, on-device learning to adapt to changing conditions.

Brett Adcock states that Figure AI's "Helix 2" neural net provides the right technical stack for general robotics. The biggest remaining obstacle is not hardware but the immense data required to train the robot for a wide distribution of tasks. The company plans to spend nine figures on data acquisition in 2026 to solve this.

The "bitter lesson" (scale and simple models win) works for language because training data (text) aligns with the output (text). Robotics faces a critical misalignment: it's trained on passive web videos but needs to output physical actions in a 3D world. This data gap is a fundamental hurdle that pure scaling cannot solve.