Companies like One X deploy robots that are remotely operated by humans to complete tasks. This strategy provides immediate value to customers while simultaneously collecting vast amounts of real-world training data, which is the primary bottleneck for developing full autonomy.

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.

Unlike consumer AI trained on public internet data, industrial AI requires vast, proprietary datasets from the physical world (e.g., sensor readings from a submarine hull). Gecko Robotics is building this data corpus via its robots, creating an advantage that's difficult to replicate.

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.

Gecko's founder realized building robots alone leads to a commoditized future. The real value was using purpose-built robots to gather unique data on infrastructure health, enabling predictive maintenance and creating a software and data moat that is difficult to replicate.

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.

To achieve scalable autonomy, Flywheel AI avoids expensive, site-specific setups. Instead, they offer a valuable teleoperation service today. This service allows them to profitably collect the vast, diverse datasets required to train a generalizable autonomous system, mirroring Tesla's data collection strategy.

To create a powerful data flywheel for AI training, ONE X estimates that deploying 10,000 robots into the world would generate a data influx comparable to the daily upload rate of YouTube. This provides a concrete benchmark for the scale required to achieve self-improving general intelligence in robotics.