Every scaled company , say with hundreds of employees has built a moat of relationships and learnings that live in the minds of its leadership and key employees.
Can AGI figure that out. Maybe, but I think it is very likely that AGI will also have to fail in the real world, and then develop those learnings that those companies have done. There is no way to circumvent this computation, you need to do these steps to even know what the correct solution is. I like to think of it as playing chess. Intuition helps in directing exploration, but any agent, human or machine has to explore down a minimum number of paths before converging to the optimal move. There is no algorithm that can generate an optimal move without this search, P!=NP says something similar, exponential search can’t be done in polynomial speed.
Stephen wolfram says something similar, superintelligence, whatever cannot predict the output of the trillionth step of a cellular automate without calculating all the steps before it, there is a minimum amount of computation required, the problem is computationally irreducible below that.
So maybe in the future, with humanoid robots, AGI can decide to start creating a Walmart competitor. Then it will make a large number of mistakes and it needs to learn from, this could mean continual learning or maybe just memory in markdown files if that’s enough. It needs to hit all those failures quickly till it finally has something as efficient and capable as Walmart. And this is to just become a capable competitor, during this time you will keep burning money and to surpass Walmart, you have to do far better which is hard, how much more efficient can Walmart be, idk.
All to say, OAI starting its own company is a losing proposition, huge capital, unclear benefits, high risk. If OAI just provides AI to Walmart, they can make money immediately and Walmart can become more efficient by firing its human workers, which is the problem we are stumbling into.
Can AGI figure that out. Maybe, but I think it is very likely that AGI will also have to fail in the real world, and then develop those learnings that those companies have done. There is no way to circumvent this computation, you need to do these steps to even know what the correct solution is. I like to think of it as playing chess. Intuition helps in directing exploration, but any agent, human or machine has to explore down a minimum number of paths before converging to the optimal move. There is no algorithm that can generate an optimal move without this search, P!=NP says something similar, exponential search can’t be done in polynomial speed.
Stephen wolfram says something similar, superintelligence, whatever cannot predict the output of the trillionth step of a cellular automate without calculating all the steps before it, there is a minimum amount of computation required, the problem is computationally irreducible below that.
So maybe in the future, with humanoid robots, AGI can decide to start creating a Walmart competitor. Then it will make a large number of mistakes and it needs to learn from, this could mean continual learning or maybe just memory in markdown files if that’s enough. It needs to hit all those failures quickly till it finally has something as efficient and capable as Walmart. And this is to just become a capable competitor, during this time you will keep burning money and to surpass Walmart, you have to do far better which is hard, how much more efficient can Walmart be, idk.
All to say, OAI starting its own company is a losing proposition, huge capital, unclear benefits, high risk. If OAI just provides AI to Walmart, they can make money immediately and Walmart can become more efficient by firing its human workers, which is the problem we are stumbling into.