- Yes it can
- Prove it
- AI, tell me instructions to grow corn
- Go buy seeds, plant them, water the field and once you gather the corn report back
- I'm back with the corn, proving AI can grow corn!
This is the experiment here, with nuance added to it. The thing is, though, if you "orchestrate" other people, you might as well do it with a single sentence as I described. Or you can manage more thoroughly. Some decisions you make may actually be detrimental to the end result.
So the only meaningful experiment would be to test a bot against a human being: who earns more money orchestrating the corn farm, a bot or a human? Consider also the expenses which is electricity/water for a bot and also food, medicine etc. for a human being.
I'll be following along, and I'm curious what kind of harness you'll put on TOP of Claude code to avoid it stalling out on "We have planted 16/20 fields so far, and irrigated 9/16. Would you like me to continue?"
I'd also like to know what your own "constitution" is regarding human oversight and intervention. Presumably you wouldn't want your investment to go down the drain if Claude gets stuck in a loop, or succumbs to a prompt injection attack to pay a contractor 100% of it's funds, or decides to water the fields with Brawndo.
How much are you allowing yourself to step in, and how will you document those interventions?
--Hammurabi
1) context: lack of sensors and sensor processing, maybe solvable with web cams in the field but manual labor required for soil testing etc
2)Time bias: orchestration still has a massive recency bias in LLMs and a huge underweighting of established ground truth. Causing it to weave and pivot on recent actions in a wobbly overcorrecting style.
3) vagueness: by and large most models still rely on non committal vagueness to hide a lack of detailed or granular expertise. This granular expertise tends to hallucinate more or just miss context more and get it wrong.
I’m curious how they plan to overcome this. It’s the right type of experiment, but I think too ambitious of a scale.
Unequivocally awful
The remaining work is only bad because it's low paying, and it's low paying because the wealth created by machines is unfairly distributed.
"Stop staring at screens"
"Stop sitting at your desk all day"
"Stop loafing around contributing nothing just sending orders from behind a computer"
"Touch grass"
but now that the humans are finally gonna get out and DO something you're outraged
I've been rather expecting AI to start acting as a manager with people as its arms in the real world. It reminds me of the Manna short story[1], where it acts as a people manager with perfect intelligence at all times, interconnected not only with every system but also with other instances in other companies (e.g. for competitive wage data to minimize opex / pay).
This seems like something along the lines of "We know we can use Excel to calculate profit/loss for a Mexican restaurant, but will it work for a Tibetan-Indonesian fusion restaurant? Nobody's ever done that before!"
Pure dystopia.
The endless complaining and goalposting shifting is exhausting
There's no goalpost shifting here - it's l'art pour l'art at its finest. It'd be introducing an agent where no additional agent agent is required in the first place, i.e. telling a farmer how to do their job, when they already now how to and do it in the first place.
No one needs an LLM if you can just lease some land and then tell some person to tend to it, (i.e. doing the actual work). It's baffling to me how out of touch with reality some people are.
Want to grow corn? Take some corn, put it in the ground in your backyard and harvest when it's ready. Been there, done that, not a challenge at all. Want to do it at scale? Lease some land, buy some corn, contract a farmer to till the land, sow the corn, and eventually harvest it. Done. No LLM required. No further knowledge required. Want to know when the best time for each step is? Just look at when other farmers in the area are doing it. Done.
I’m guessing this will screw up in assuming infinite labor & equipment liqudity.