The video models are the ones that seem to be attracting the most attention in this area as it seems do similar to sight recognition.
Rather the opposite, I'd say: existing manufacturing automation is built around repetitive motions because an assembly line is making multiples of the same product. Having AI reinvent the wheel for every individual item is completely pointless.
One-off manufacturing can to a certain extent be automated. We're already seeing that with things like 3D printing and dirt-cheap basic PCB assembly. However, in most cases economies of scale prevent that from widespread generalization to entire products: ordering 100 or 1000 is always going to be have significantly lower per-unit costs than ordering 1, and if you're ordering 1000 you can probably afford a human spending some time on setting up robots or optimizing the design for existing setups.
There are undoubtedly some areas where the current AI boom can provide helpful tooling, but I don't expect it to lead to a manufacturing revolution.
Imagine a future where any hardware startup could design and provision an assembly line as easily and cheaply as software startups today use cloud computing. Maybe after a certain scale it becomes economical to consider replacing steps of the manufacturing process with "ASIC" solutions, but maybe there'd be a long tail of things which would continue to remain best served by general-purpose robots indefinitely.
It avoids the need for any sort of parts shipping, and can be easily retooled to make war machines in a time of emergency (which is one of the motivations to bring back american manufacturing).
The main problem with this is that you still need surface-mount components if you are going to make PCBs, and you still need magnets to make motors, etc.
Training these models takes a bunch more time, because you first need to build special hardware that allows a human to do these motions while having a computer record all the sensor inputs and outputs, and then you need to have the human do them a few thousand times, while LLMs just scrape all the content on the Internet. But it's potentially a lot more impactful, because it allows robots to impact the physical world and not just the printed word.
As long as you do that, the penalty for a a slop-based fuckup is just a less efficient toolpath.
The more we can bring down all the difficulty of all these processes, the more we can accelerate manufacturing locally.
That final "millions" is the problem. Automation is great and easy when you will do the same thing millions of times. Sure it might cost half a million to program the robot (which itself cost half a million) - but that is $1.00 per part, and it goes down as you make more. When you are only building 10 though a million dollars is a lot of money and so you want humans - or robots that are "CAPABLE of plannings its own motion".
Costs have been going down. In high school I took the class on how to write g-code (I have one free period so I took shop for non-college bound kids for fun even though I was college bound - it was a great time that I highly recommend even though it was only for fun). These days almost everyone just uses their CAD/CAM and isn't even aware that the g-code is supposed to be a human readable programming language. (it probably isn't)
https://www.telegraph.co.uk/business/2025/10/12/why-western-...
Not really. The robots are programmed by having a human manually guide it, so the robot itself doesn't really have to do any navigation - it just has to follow a predefined path.
Want to install different variants of dash components? Split it up into methods and have the robot return to a neutral position after each method. You're literally programming it.
I think there's something cultural about wanting office jobs related to power over people, where you can always slack instead of waking up every day at 8 to go to the factory
Not an American myself, but why should that be good for ordinary American citizens?
Few people make loads of money, some Gen-Xer secure the value of their 401k and the younger ones are out of job?
There is the constant argument that what when machines do everything. We are not there yet, and so far there is no reason to think we will be anytime soon.
If only. In reality they'll be as expensive as they can make them without completely killing sales, just like they are right now.
Aren’t the creative jobs also being taken by LLMs and image generators?
If that's true, why isn't unrestricted immigration[1] good for them? It means that the citizens don't have to do the boring immigrant jobs, but still get the benefits for vast amounts of immigrant-produced goods and services.
The only ones who will lose out are ones who 'want to'[2] do the boring immigrant jobs.
AI can't just handwave all this shit away because 'technology good'. Whether or you agree with these concerns or not, there's a massive backlash from various flavors of nativists about jobs. Why isn't it directed at all of these pie in the sky AI promises?
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[1] Or, you know, just buying imports from China. What difference does it make to me where a factory is located, when that factory doesn't employ me or my neighbours? The people collecting profits from it aren't going to share them with us.
[2] What does it mean to 'want to' do a 'boring' job? Rent's due in two weeks, 'wants' don't enter into it much.
Yes! I'm pretty sure the guy working his ass of at the factory does so because brain surgeon doesn't pay enough...
Is this the next version of "trickle down economics"?
It's having the same sort of impact as unlimited immigration, except that in this case, the workers don't need weekends, or pay taxes.
AI is making almost no difference in hiring at all.
Western executives who visit China are coming back terrified - https://news.ycombinator.com/item?id=45563018 - October 2025
Was Made in China 2025 Successful? [pdf] - https://www.uschamber.com/assets/documents/Was-Made-in-China... - May 5th, 2025
ASPI’s two-decade Critical Technology Tracker: The rewards of long-term research investment - https://www.aspi.org.au/report/aspis-two-decade-critical-tec... - August 28th, 2024
> Now covering 64 critical technologies and crucial fields spanning defence, space, energy, the environment, artificial intelligence (AI), biotechnology, robotics, cyber, computing, advanced materials and key quantum technology areas, the Tech Tracker’s dataset has been expanded and updated from five years of data (previously, 2018–2022) to 21 years of data (2003–2023). These new results reveal the stunning shift in research leadership over the past two decades towards large economies in the Indo-Pacific, led by China’s exceptional gains. The US led in 60 of 64 technologies in the five years from 2003 to 2007, but in the most recent five years (2019–2023) is leading in seven. China led in just three of 64 technologies in 2003–2007 but is now the lead country in 57 of 64 technologies in 2019–2023, increasing its lead from our rankings last year (2018–2022), where it was leading in 52 technologies.