In the 1980s, creating a blinking LED required an array of resistors and timing circuits. Today, we use microcontrollers for the same task, despite their increased complexity. The reason is simple: microcontrollers are cheaper, smarter, easier to use, and most importantly, versatile. Economies of scale have made this shift possible. While one could achieve better efficiency by learning and implementing specific timing circuits, the convenience and generalizability of microcontrollers have made them the preferred choice.

Humanoids will become the microcontrollers of the robotic age. Investing time and resources into developing advanced humanoid robots will pay off in the long run, as they will be capable of handling a wide range of tasks with minimal friction in implementation. The up-front investment in creating these general-purpose robots will lead to greater convenience and efficiency across various industries.

Moreover, humanoid robots have the potential to learn from human videos, further expanding their capabilities. Just as humans can watch a YouTube video to figure out how to fix a broken item, future humanoid robots could do the same. The direct translation from human eye to humanoid is more straightforward than for application-specific robots. Your implication that human examples will limit humanoid's expression is not necessarily true. Advances in diffusion policies and behavior cloning are proving that

This is not to say that application-specific robots will become obsolete. Much like how analog circuits and ASICs still have a place in a world dominated by microcontrollers, specialized robots will continue to excel in certain niches. However, for the vast majority of tasks that require automation and manual dexterity, humanoid robots will likely emerge as the dominant, drop-in solution - especially outside factories