Sorry, but warehouse pickers and packers are not, and will never be at risk from LLMs.
Because they’re already obsolete from standard 30 year old robotics.
Also anything requiring precision, suited and accuracy isnt ever going to be viable for LLMs to replace. The technology isn’t designed for that and is not capable of meeting a human.
E.G. for general automaton: US automotive giants Ford and GM tried to go fully automated for production in the 1980s and 1990s, but reverted some of the automation when it turned out that their senior machinists were better and faster than the robots, saving the companies more than a million dollars per person per year.
I think this comment misses the mark on a few points. Let me break it down.
First off, LLMs aren’t meant to physically replace warehouse pickers and packers – that’s not the point. What they can do is supercharge the automation we already have. LLMs can manage logistics, predict inventory, optimize warehouse layouts, and even coordinate robotic systems more efficiently. So while the robots might be doing the heavy lifting, LLMs are the brains that help them work smarter and faster.
Now, about this idea that 30-year-old robotics have already made pickers obsolete – that’s not quite right. Sure, we’ve had robots for decades, but the tech has come a long way since then. Early automation was clunky and limited, but modern robots? They’ve got AI-driven vision, flexible grippers, and adaptive systems that let them handle all kinds of tasks, even things as precise as packing odd-shaped items. Amazon, for example, already uses AI-powered robotic arms in their warehouses, and they’re getting better every year.
As for precision – I get why you’d think LLMs aren’t up to that, but they actually play a huge role in making robots more precise. LLMs can process sensor data, adjust algorithms on the fly, and help robots fine-tune their movements. It’s not about replacing humans directly – it’s about helping robots learn and adapt faster.
The Ford and GM example is interesting, but it’s a bit outdated. Sure, back in the 80s and 90s, machinists could outperform the robots, but that’s not the case anymore. Tesla’s Gigafactories, Amazon’s fulfillment centers – modern automation often outpaces human workers now, both in speed and accuracy. The human role is shifting more towards overseeing and maintaining these systems, rather than competing with them directly.
And let’s not forget – warehousing is one of the fastest sectors to automate right now. E-commerce giants are investing heavily in robotic solutions to pick, pack, and sort, and LLMs are driving that forward by managing and optimizing the whole process. The more we lean into AI and automation, the less we need manual labor in these environments.
So yeah, LLMs aren’t coming for warehouse jobs by themselves – but they’re definitely helping push automation to a level where fewer humans are needed. It’s not a far-off future, it’s already happening.
Show me documentation of any of this actually happening and being effective. @
E.G. Dell has had automated logistics for more than 20 years. LLMs would make it less efficient, since they aren’t anywhere near as fast or efficient as regular programs. And they hallucinate. Ditto Ikea and a few others for that matter.
E.G.2. LLMs cannot and will not “fine tune” robotic movements. The movement of a robotic arm is either hand-programmed, or done with a mathematical process called Inverse Kinematics to move them between two points. They are already fine tuned.
You don’t need vision systems in a warehouse. That’s what QR and barcode scanners are for.
It doesn’t necessarily contradict but adds nuance to the conversation. LLMs shine in areas like logistics, data analysis, and workflow automation, despite their role in direct robotic control or real-time precision tasks is limited.
Where the confusion might arise is that while LLMs can contribute to robotics—like interpreting natural language commands or generating code—they aren’t a substitute for core movement algorithms like inverse kinematics. In other words, LLMs enhance certain aspects around robotics and automation but don’t replace the specialized systems already in place for critical tasks.
The focus is more on integration and augmentation, not replacement.
Sorry, but warehouse pickers and packers are not, and will never be at risk from LLMs.
Because they’re already obsolete from standard 30 year old robotics.
Also anything requiring precision, suited and accuracy isnt ever going to be viable for LLMs to replace. The technology isn’t designed for that and is not capable of meeting a human. E.G. for general automaton: US automotive giants Ford and GM tried to go fully automated for production in the 1980s and 1990s, but reverted some of the automation when it turned out that their senior machinists were better and faster than the robots, saving the companies more than a million dollars per person per year.
I think this comment misses the mark on a few points. Let me break it down.
First off, LLMs aren’t meant to physically replace warehouse pickers and packers – that’s not the point. What they can do is supercharge the automation we already have. LLMs can manage logistics, predict inventory, optimize warehouse layouts, and even coordinate robotic systems more efficiently. So while the robots might be doing the heavy lifting, LLMs are the brains that help them work smarter and faster.
Now, about this idea that 30-year-old robotics have already made pickers obsolete – that’s not quite right. Sure, we’ve had robots for decades, but the tech has come a long way since then. Early automation was clunky and limited, but modern robots? They’ve got AI-driven vision, flexible grippers, and adaptive systems that let them handle all kinds of tasks, even things as precise as packing odd-shaped items. Amazon, for example, already uses AI-powered robotic arms in their warehouses, and they’re getting better every year.
As for precision – I get why you’d think LLMs aren’t up to that, but they actually play a huge role in making robots more precise. LLMs can process sensor data, adjust algorithms on the fly, and help robots fine-tune their movements. It’s not about replacing humans directly – it’s about helping robots learn and adapt faster.
The Ford and GM example is interesting, but it’s a bit outdated. Sure, back in the 80s and 90s, machinists could outperform the robots, but that’s not the case anymore. Tesla’s Gigafactories, Amazon’s fulfillment centers – modern automation often outpaces human workers now, both in speed and accuracy. The human role is shifting more towards overseeing and maintaining these systems, rather than competing with them directly.
And let’s not forget – warehousing is one of the fastest sectors to automate right now. E-commerce giants are investing heavily in robotic solutions to pick, pack, and sort, and LLMs are driving that forward by managing and optimizing the whole process. The more we lean into AI and automation, the less we need manual labor in these environments.
So yeah, LLMs aren’t coming for warehouse jobs by themselves – but they’re definitely helping push automation to a level where fewer humans are needed. It’s not a far-off future, it’s already happening.
Show me documentation of any of this actually happening and being effective. @
E.G. Dell has had automated logistics for more than 20 years. LLMs would make it less efficient, since they aren’t anywhere near as fast or efficient as regular programs. And they hallucinate. Ditto Ikea and a few others for that matter. E.G.2. LLMs cannot and will not “fine tune” robotic movements. The movement of a robotic arm is either hand-programmed, or done with a mathematical process called Inverse Kinematics to move them between two points. They are already fine tuned.
You don’t need vision systems in a warehouse. That’s what QR and barcode scanners are for.
It doesn’t necessarily contradict but adds nuance to the conversation. LLMs shine in areas like logistics, data analysis, and workflow automation, despite their role in direct robotic control or real-time precision tasks is limited.
Where the confusion might arise is that while LLMs can contribute to robotics—like interpreting natural language commands or generating code—they aren’t a substitute for core movement algorithms like inverse kinematics. In other words, LLMs enhance certain aspects around robotics and automation but don’t replace the specialized systems already in place for critical tasks.
The focus is more on integration and augmentation, not replacement.