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.

Unlike cloud-reliant AI, Figure's humanoids perform all computations onboard. This is a critical architectural choice to enable high-frequency (200Hz+) control loops for balance and manipulation, ensuring the robot remains fully functional and responsive without depending on Wi-Fi or 5G connectivity.

While often discussed for privacy, running models on-device eliminates API latency and costs. This allows for near-instant, high-volume processing for free, a key advantage over cloud-based AI services.

Figure chose to develop its AI systems in-house rather than rely on its partnership with OpenAI. The reason was that its own team proved superior at the highly specialized task of designing, embedding, and running models on physical robot hardware, a challenge distinct from training purely digital LLMs.

Figure determined that coding robot movements is unscalable due to the infinite possible states (360^40). They pivoted from traditional C++ to Helix, an AI policy that controls the robot's entire body from camera inputs, treating robotics as a neural network problem, not a software engineering one.

While often used interchangeably, 'Physical AI' is more specific than 'Edge AI.' Edge AI broadly concerns processing data locally. Physical AI refers to edge systems, like robots or autonomous vehicles, that not only sense and predict but also execute physical actions based on those predictions.

While on-device AI for consumer gadgets is hyped, its most impactful application is in B2B robotics. Deploying AI models on drones for safety, defense, or industrial tasks where network connectivity is unreliable unlocks far more value. The focus should be on robotics and enterprise portability, not just consumer privacy.

Samsara's AI systems, like in-cab cameras, are built to function without connectivity for extended periods (e.g., a week). They gracefully degrade and sync when back online, a crucial feature for industries like utilities construction working in areas without roads or cell signals.

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.