Once robots can do physical jobs, how quickly could they become a significant part of the work force?
Here’s a couple of fermi estimates, showing (among other things) that converting car factories might be able to produce 1 billion robots per year in under 5 years.
Nothing too new here if you’ve been following Carl Shulman for years, but I thought it could be useful to have a reference article. Please let me know about corrections or other ways to improve the estimates.
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.
I’m curious how good current robots are compared to where they’d need to be to automate the biggest bottlenecks in further robot production. You say we start from 10,000/year, but is it plausible that all current robots are too clumsy/incapable for many key bottleneck tasks, and that getting to 10,000 sufficiently good robots produced per year might be a long path—e.g. it would take a decade+ for humans? Or are current robots close to sufficient with good enough software?
I also imagine that even taking current robot production processes, the gap between a WW2-era car factory and a WW2-era combat airplane factory might be much smaller than the gap between a car factory and a modern frontier robotics factory, I imagine they are a big step up in complexity.
My impression is that software has been the bottleneck here. Building a hand as dextrous as the human hand is difficult but doable (and has probably already been done, though only in very expensive prototypes); having the software to actually use that hand intelligently and deftly as a human would has not yet been done. But I’m not an expert. Power supply is different—humans can work all day on a few Big Macs, whereas robots will need to be charged, possibly charged frequently or even plugged in constantly. But that doesn’t seem like a significant obstacle.
Re: WW2 vs. modern: yeah idk. I don’t think the modern gap between cars and humanoid robots is that big. Tesla is making Optimus after all. Batteries, electronics, chips, electric motors, sensors… seems like the basic components are the same. And seems like the necessary tolerances are pretty similar; it’s not like you need a clean room to make one but not the other, and it’s not like you need hyperstrong-hyperlight exotic materials for one but not the other. In fact I can think of one very important, very expensive piece of equipment (the gigapress) that you need for cars but not for humanoid robots.
All of the above is for ‘minimum viable humanoid robots’ e.g. robots that can replace factory and construction workers. They might need to be plugged in to the wall often, they might wear out after a year, they might need to do some kinds of manipulations 2x slower due to having fatter fingers or something. But they don’t need to e.g. be capable of hiking for 48 hours in the wilderness and fording rivers all on the energy provided by a Big Mac. Nor do they need to be as strong-yet-lightweight as a human.
Individual humans can make pretty cool mechanical hands — see here. That strongly suggests that dexterous robot hands can make dexterous robot hands, enabling exponential growth even without spinning up new heavy machinery and production lines, I figure.
In the teleoperated robots category (which is what we should be talking about if we’re assuming away algorithm challenges!), Ugo might or might not be vaporware but they mention a price point below $10/day. There’s also the much more hardcore Sarcos Guardian XT (possibly discontinued??). Pricing is not very transparent, but I found a site that said you lease it for $5K/month, which isn’t bad considering how low the volumes are.
+1, and also you might be able to get away with being clumsy and slow in many cases as long as the software is smart enough to figure out a way to do the thing eventually.
Thanks, great comment.
Seems like we roughly agree on the human-only case. My thinking was that the profit margin would initially be 90-99%, which would create huge economic incentives. Though incentives and coordination were probably stronger in WW2, which could make things slower. Also 10x per year for 5 years sounds like a lot – helpful to point out they didn’t quite achieve that in WW2.
With ASI, I agree something like another 5x speed-up sounds plausible.
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.
I think the range is as follows:
Estimates based on looking at how fast humans can do things (e.g. WW2 industrial scaleup) and then modifying somewhat upwards (e.g. 5x) in an attempt to account for superintelligence… should be the lower bound, at least for the scenario where superintelligence is involved at every level of the process.
The upper bound is the Yudkowsky bathtub nanotech scenario, or something similarly fast that we haven’t thought of yet. Where the comparison point for the estimate is more about the laws of physics and/or biology.
Oh yes—to the extent we have significantly greater-than-human intelligence involved, adapting existing capacities becomes less of an issue. It only really remains an issue if there’s a fairly or very slow takeoff.
This is increasingly what I expect; I think the current path toward AGI is fortunate in one more way: LLMs probably have naturally decreasing returns because they are mostly imitating human intelligence. Scaffolding and chain of thought will continue to provide routes forward even if that turns out to be true. The evidence loosely suggests it is; see Thane Ruthenis’s recent argument and my response.
The other reason to find slow takeoff plausible is if AGI doesn’t proliferate, and its controllers (probably the US and Chinese governments, hopefully not too many more) are deliberately limiting the rate of change, as they probably would be wise to do—if they can simultaneously prevent others from developing new AGI and putting the pedal to the metal.
Thanks, and fair points!
Note that it you convert only half the car factories, you can still produce 0.5 billion robots per year, so it doesn’t change the basics picture that much. (It’s all order of magnitude stuff.)
I talk a little about some other estimates—a standard trajectory would be 20-30 years on the long end. ASI enabled could be even faster than 5yr. I agree it would be nice to flesh these out more.
Also agree it would be good to figure out the conversion efficiency better. One factor on the other side is robots involve lighter parts, which apparently makes it easier. Ideally we’d also check for other input factors that could bottleneck production -eg lithium for batteries at over 100m.
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.
Yes—if anyone reading knows more about manufacturing and could comment on how easy it would be to convert, that would be very helpful.
I also agree it would be interesting to try to do more analysis of how much ASI and robotics could speed up construction of robot factories, by looking at different bottlenecks and how much they could help.
I’m not sure a robot workforce would have a huge effect initially, since there’s already a large pool of human workers (though maybe you get some boost by making everything run 24⁄7). However, at later stages it might become hard to hire enough human workers, while with robots you could keep scaling.
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.
Thanks for the comments!
I’m especially keen to explore bottlenecks (e.g. another suggestion I saw is that to reach 1 billion a year would require 10x current global lithium production to supply the batteries.)
A factor of 2 for increased difficultly due to processing intensity seems reasonable, and I should have thrown it in. (Though my estimates were to an order of magnitude so this probably won’t change the bottom line, and on the other side, many robots will weigh <100kg and some will be non-humanoid.)
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 did wonder about maintenance costs, but I figured they wouldn’t change the picture too much because I only assume an avg 3 year lifetime for the robot, and figured they wouldn’t need a huge amount of maintenance to make it to that point.
Moreover, if there’s worthwhile maintenance that extends the lifetime further, then the hardware costs could end up cheaper than my per year estimate.
I’m also envisioning the costs after a big scale up, and there would be robot repair shops as numerous as car repair, rather than needing to fly in specialists.
That said, I agree it would be interesting to look at how much is spent on car maintenance per year on a car vs. capital costs. (I expect it would be under 10%?)
The average American drives 45,000 miles in three years, but a car operated 20⁄7 (like your robot) would accumulate about a million miles in that timeframe. Probably it would go through 2 engines and 3 transmissions if it could even be kept on the road. All things being equal it would need 22x as much maintenance than the average of the US fleet, so probably more like 220% of the capital cost.
A really nice printer/photocopier-combo costs about $10,000 like your robot, and is built from of motors, cameras, and computers just like your robot. While it’s mature technology and built to generally high quality standards, if you try running copies 24⁄7 you will quickly be on a first-name basis with local Kyocera guy.
That’s helpful! Makes me think the all in hardware costs could be off by a factor of 2x.
Signalling that I do not like linkposts to personal blogs.
My take is it’s fine/good, but the article is much more likely to be read (by me and many others) if the full content is crossposted (or even the opening bunch of paragraphs).
I’d be happy to put the opening bunch of paragraphs. I was feeling reluctant to cross-post because I often update my articles as I learn more about a topic, and I don’t want to keep multiple versions in sync (especially for a lower priority article).
That makes sense. We have something of a solution to this where users with RSS crossposting can manually re-sync the post from the triple-dot memu. I’ll DM you about how to set it up if you want it.