Like jerpint said, there's lots of limitations here still since this only addresses the high level decision making part, not the low-level control (sending sign

Like jerpint said, there's lots of limitations here still since this only addresses the high level decision making part, not the low-level control (sending signals to motors). Not tackling low-level control means the robot is reliant on human-engineered primitives such as 'place' or 'push', which can do a lot but is not fully generic (there are tasks that require dexterous control, tight visuomotor control loops, etc.). In other words, this kind of system is not even close to being general enough to drive a car. Also, this has the typical limitations of LLMs - no (long term) memory, inherently limited context window size, no online learning via eg trial and error, no 'agency' in general (it can only do things when it is told to, there is no sense of exploring the world and learning about it). To really become AGI these sorts of systems would really need to be able to adapt to lots of niche tasks that they don't apriori know how to do, and there's not a great solution for that right now (prompting can sort of work, but again memory/context window size is still an issue).