I'd be interested in an attempt to zoom in specifically on the "repurpose existing factories to make robots" part of the story. You point to WW2 car companies turning into tank and plane factories, and then say maybe a billion humanoid robots per year within 5 years of the conversion.

My wild guesses:

Human-only world: Assume it's like ww2 all over again except for some reason everyone thinks humanoid robots are the main key to victory:

Then yeah, WW2 seems like the right comparison here. Brief google and look at some data makes me think maybe combat airplane production scaled up by an OOM in 1-2 years early on, and then tapered off to more like a doubling every year. I think what this means is that we should expect something like an OOM/year of increase in humanoid robot production in this scenario, for a couple years? So, from 10,000/yr (assuming it starts today) to a billion/yr 5 years later?

ASI-powered world: Assume ASIs are overseeing and directing the whole process + government is waiving red tape etc. (perhaps because ASI has convinced them it's a good idea):

So obviously things will go significantly faster with ASI in charge and involved at every level. The question is how much faster. Some thoughts:

• ASI probably needs far less on-the-job experience than human companies do, to reach the same level of know-how. Like, maybe if you let it ingest all the data from Tesla, Boston Dynamics, Ford, GM, SpaceX, etc. collected over the past two decades, and analyze all that data etc., and if you give it the blueprints and prototypes for the current humanoid robots, it can in a week spit out a blueprint and plan for how to refit existing factories to produce mildly-improved versions of said robots at a run rate of about a million/yr, the plan taking six months to execute on in practice. (So this would mean 2 OOMs in 6 months whereas in the human-only world I was guessing 1 OOM in a year.)
• Think about how much faster Elon & his companies seem to be able to get things done compared to various legacy companies, and extrapolate -- seems fair to assume that ASI would be at least as far above Elon as Elon is above typical competitor companies. Probably in fact that's a super conservative assumption. "But muh bottlenecks" --> "The whole point is we are trying to estimate how harshly the bottlenecks bite. They evidently don't bite harshly enough to stop SpaceX from seemingly going like 5x faster than Blue Origin." Also, Elon is only one guy, and his companies have a limited number of employees thinking, at human speed who can't just copy themselves like ASI could.
• There's also 'sci-fi' stuff to consider like nanobots etc. I think this should be taken seriously, much more seriously than people outside MIRI seem to take it. I think we basically don't have a way to upper bound how fast things could go post-ASI, or rather, I think the upper bound looks like Yudkowsky's bathtub nanotech story. 

Overall I'd guess that we would get to a billion/yr humanoid robot production within about a year of ASI, and that the bulk of these robots would be substantially more sophisticated as well compared to present-day robots. And it's easier for me to imagine things going faster than that, than slower, though perhaps I should also account for various biases that push in the other direction. For now I'll just hand-wave and hope it cancels out.

This is highly useful, thank you! It will be my reference article for this pretty critical point for world modeling the near future.

If you want to tinker with estimates at all:

You shouldn't have all auto factories converting; there will still be demand for cars, and more if there's less production.

In general it would be helpful to have a range of estimates.

Kilogram estimates of car-robot are fine but it seems like there should be a large adjustment for robots having more different motors and joints than a whole car.

On one side: Humanoid robots have much more density of parts requiring more machine-time than cars, probably slowing things a bunch.

On the other, you mention assuming no speed up due to the robots building robot factories, but this seems like the dominant factor in the growth. Your numbers excluding that are going to be way underestimating things pretty quickly without that. I'd be interested in what those numbers look like assuming reasonable guesses about robot workforce being part of a feedback cycle.

I like this effort, and I have a few suggestions:

• Humanoid robots are much more difficult than non-humanoid ones. There are a lot more joints than in other designs; the balance question demands both more capable components and more advanced controls; as a consequence of the balance and shape questions, a lot of thought needs to go into wrangling weight ratios, which means preferring more expensive materials for lightness, etc.
• In terms of modifying your analysis, I think this cashes out as greater material intensity - the calculations here are done by weight of materials, we just need a way to account for the humanoid robot requiring more processing on all of those materials. We could say something like 1500kg of humanoid robot materials take twice as much processing/refinement as 1500kg of car materials (occasionally this will be about the same; for small fractions of the weight it will be 10x the processing, etc).
• The humanoid robots are more vulnerable to bottlenecks than cars. Specifically they need more compute and rare earth elements like neodymium, which will be tough because that supply chain is already strained by new datacenters and AI demands.

Fly into Monterrey Mexico sometime and notice who is on the flight with you: Nearly everyone will be a technician-looking guy in his 30s or 40s. The times I've done it, probably 80% of the flight (including me and my colleagues) fit that criteria. The eastern side of that city is packed wall-to-wall with shiny new manufacturing plants, each filled to the brim with the latest and greatest in industrial automation, robots not excluded. Many of those foreign technician-looking guys are wearing polo shirts emblazoned with the logos of the companies which manufactured that equipment, and they are on their way to service it.

The author's analysis leaves out the service cost for these robots. What happens when a servo motor or touch sensor malfunctions? Probably 2 technician-looking guys in robot-company polo shirts have to fly in, maybe even from overseas, and bill somebody for 3 days each of hotel stay and dining per diem, on top of the cost of their flight and salary.

Sure, this would become less of an issue if a standard design of robot becomes widely diffused, but consider today how even routine car maintenance can easily cost hundreds of dollars.

The author might suppose that general-purpose robots could be manufactured which is able to diagnose and repair other general-purpose robots---AI will undoubtedly mature to this level in the near future, but sensors and electromechanical elements won't move so quickly. I expect huge advancement in automation over the next few decades, and huge hiring and training of humans needed to keep the robots alive and doing their jobs.

I wrote a LessWrong article that tries to estimate doubling time for a self-reproducing robot.  A critical step is that smaller robots are faster.  Most manufacturing processes scale such that they get N times faster as they get N times smaller.  I picked N=4, for reasons explained in the article.  I concluded the doubling time is five weeks.   So the time to a billion robots is on the order of five years.

Even if your goal is a human-size robot, you’re better off building small robots to build it, since they work faster.  I assumed fairly clumsy hardware, but software comparable to a human machinist in cleverness.

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