But then you need one robot that's got 9,999 specializations. A human can't actually do 10,000 things. You need 10,000 humans, each that goes through a specialization process (training, developing muscle memory, etc) that builds both physiology and mental capability specific to the skill. Not only does the robot need to be capable of every incredibly difficult physical skill we learn, it needs a matching program.

It's impossible to do this in a general way. This could theoretically be scalable (produce the robot and have 10,000 companies all develop their own specialization routines), but the hardware (both the parts as well as neural interface) needs to be as capable as a human body, which isn't even remotely true. The physical robot will always limit what skills it can learn, on top of the difficulty of programming the skill.

I think we're hundreds of years away from making a robot that's as capable as a human. We would get there faster with synthetics or cyborgs. Create a human body without a brain, use Neuralink to operate it. Until then, specialized robots are the only thing that will scale to 10,000 skills.

The words you're looking for are: "transfer learning".

Currently, dedicated robotics datasets are pathetic - in both the raw size and domain diversity - compared to what we have for generative AIs in domains like text, sound, video or images. So adding any more data helps a lot.

If you trained a robot to fully strip down a specific e-scooter model - whether for repair, remanufacturing or recycling - that training data would then help with any similar tasks. As well as a variety of seemingly unrelated tasks that also require manual dexterity, manipulation and spatial reasoning.

Those "9999 specializations" all overlap in obvious and subtle ways - and they feed little bits of skills and adaptations to each other. Which is why a lot of the robotic companies are itching to start pushing the units out there as soon as they are able to perform some useful tasks. They want that real world training data.