Even in real assembly lines at physical factories, I get the impression that generalization has often worked well because it gives you the ability to change your process. Toyota is/was considered best-in-class, and a major innovation of theirs was having workers rotate across different areas, becoming more generalized and more able to suggest improvements to the overall system, with some groups rotating every 2 hours[1].
Tesla famously reduced automation around 2018 even when the marginal costs were lower than human operators, again because the lost flexibility wasn’t worth it.[2] Though it’s worth noting they started investing more in robots again in recent years, presumably when their process was more solidified[3].
Even in real assembly lines at physical factories, I get the impression that generalization has often worked well because it gives you the ability to change your process. Toyota is/was considered best-in-class, and a major innovation of theirs was having workers rotate across different areas, becoming more generalized and more able to suggest improvements to the overall system, with some groups rotating every 2 hours[1].
Tesla famously reduced automation around 2018 even when the marginal costs were lower than human operators, again because the lost flexibility wasn’t worth it.[2] Though it’s worth noting they started investing more in robots again in recent years, presumably when their process was more solidified[3].
[1]: https://michelbaudin.com/2024/02/07/toyotas-job-rotation-policy/
[2]: https://theconversation.com/teslas-problem-overestimating-automation-underestimating-humans-95388
[3]: https://newo.ai/tesla-optimus-robots-revolutionize-manufacturing/
Appreciate the empirical data! This aligns with my models in the space.