The integration of AI into human-led services will mirror Tesla's approach to self-driving. Humans will remain the primary interface (the "steering wheel"), while AI progressively automates backend tasks, enhancing capability rather than eliminating the human role entirely in the near term.

Instead of creating bespoke self-driving kits for every car model, a humanoid robot can physically sit in any driver's seat and operate the controls. This concept, highlighted by George Hotz, bypasses proprietary vehicle systems and hardware lock-in, treating the car as a black box.

Companies developing humanoid robots, like One X, market a vision of autonomy but will initially ship a teleoperated product. This "human-in-the-loop" model allows them to enter the market and gather data while full autonomy is still in development.

While autonomous driving is complex, roboticist Ken Goldberg argues it's an easier problem than dexterous manipulation. Driving fundamentally involves avoiding contact with objects, whereas manipulation requires precisely controlled contact and interaction with them, a much harder challenge.

The adoption of humanoid robots will mirror that of autonomous vehicles: focus on achievable, single-task applications first. Instead of a complex, general-purpose home robot, the market will first embrace robots trained for specific, repeatable industrial tasks like warehouse logistics or shelf stocking.

Unlike pre-programmed industrial robots, "Physical AI" systems sense their environment, make intelligent choices, and receive live feedback. This paradigm shift, similar to Waymo's self-driving cars versus simple cruise control, allows for autonomous and adaptive scientific experimentation rather than just repetitive tasks.

While Figure's CEO criticizes competitors for using human operators in robot videos, this 'wizard of oz' technique is a critical data-gathering and development stage. Just as early Waymo cars had human operators, teleoperation is how companies collect the training data needed for true autonomy.

Self-driving cars, a 20-year journey so far, are relatively simple robots: metal boxes on 2D surfaces designed *not* to touch things. General-purpose robots operate in complex 3D environments with the primary goal of *touching* and manipulating objects. This highlights the immense, often underestimated, physical and algorithmic challenges facing robotics.

Car companies are uniquely positioned to build humanoid robots. They possess deep expertise in mass manufacturing complex systems with chips and batteries, and they are already heavy users of robotics in their own factories, giving them a significant advantage in the emerging market.