A significant portion of content released by competitors in the humanoid space is not autonomous. Instead, the robots are being remotely controlled (teleoperated) by a human. This is a crucial, often hidden, detail that misrepresents the true state of a company's AI capabilities.
Figure founder Brett Adcock, previously of Archer Aviation, states that electric aircraft technology is viable today. The primary gating factor for widespread adoption is the lengthy and complex safety, certification, and policy process with federal bodies like the FAA in the US and EASA in Europe.
The economic impact of humanoids goes beyond capturing the TAM of human labor. By introducing millions of 'synthetic humans' into the economy, they will fundamentally change the 'per capita' basis of GDP. This creates a potential for unbounded GDP growth, as productivity will no longer be limited by the human population.
The experience of building an electric aircraft, described as a "flying robot," is directly transferable to humanoid robotics. Both require deep expertise in integrating batteries, motors, embedded systems, sensors, and control software, creating a natural pathway for talent and knowledge between the two deep-tech fields.
Figure is intentionally designing its robots to avoid two extremes: menacing appearances and overly friendly looks with "googly eyes." The goal is to position the humanoid as a sophisticated, high-end piece of technology—a tool for humanity—rather than trying to fool users into thinking it's a toy or a person.
Contrary to conventional wisdom, pursuing massive, hard-to-solve ideas makes it easier to attract capital and top talent. Investors prefer the binary risk-reward of huge outcomes, and the best employees want to work on world-changing problems, not incremental improvements like a new calendar app.
Contrary to popular belief, China is not ahead in the humanoid race. The current bottleneck is solving general-purpose AI and systems integration, not manufacturing at scale. In this domain, US companies are leading. Manufacturing humanoids is closer to consumer electronics than cars, mitigating China's automotive-style manufacturing advantages.
A humanoid robot with 40 joints has more potential positions than atoms in the universe (360^40). This combinatorial explosion makes it impossible to solve movement and interaction with traditional, hard-coded rules. Consequently, advanced AI like neural networks are not just an optimization but a fundamental necessity.
Manipulating deformable objects like towels was long considered one of the final, hardest challenges in robotics due to their infinite variations. The fact that Figure's neural networks can now successfully fold laundry indicates that the core technological hurdles for truly general-purpose robots have been overcome.
Current devices like phones and computers were designed before the advent of human-like AI and are not optimized for it. Figure's founder argues that this creates a massive opportunity for a new class of hardware, including language devices and humanoids, which will eventually replace today's dominant form factors.
Initially, factories seemed like the easier first market for humanoids due to structured environments. However, Figure's founder now believes the home is a more near-term opportunity. The challenge of environmental variability is now seen as a data-bound problem that can be solved with large-scale data collection programs.
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.
Figure is observing that data from one robot performing a task (e.g., moving packages in a warehouse) improves the performance of other robots on completely different tasks (e.g., folding laundry at home). This powerful transfer learning, enabled by deep learning, is a key driver for scaling general-purpose capabilities.
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.