Humanoid robot technologies are advancing rapidly, but their adoption will be uneven across use cases and geographies, unlike the explosive boom of generative AI, according to research. At the Consumer Electronics Show in January 2025, Nvidia CEO Jensen Huang declared, “The ChatGPT moment for robotics is coming,” yet the research suggests a more complex, jagged path to scale. Three fundamental forces shape this different adoption curve. First, there is no one-size-fits-all model: humanoid robots will not be commercially deployed as universal productivity tools in the foreseeable future. Different roles impose distinct and often incompatible requirements. For example, a warehouse robot may need to lift up to 132 pounds, requiring sophisticated actuators that will make up 40% to 60% of its cost. A care assistant robot, however, requires subtle facial expression, fine motor control, and emotionally sensitive interaction, tilting the build toward perception and haptic technologies. This divergence extends into software and connectivity: a security robot uses low-latency edge-computing data processing to respond rapidly to environmental changes, while a hospital assistant may rely on large graphical models to navigate facilities and create 3D images of patient charts and X-rays. Each scenario requires different computing architectures, supporting an array of humanoid variations. Leaders need to reset their strategies for this complex path.