It's not that it was uneconomical, it's that 1) we literally don't know all the rules, a lot of it is learned intuition that humans acquire by doing, and 2) as task complexity rises, the number of rules rises faster, so it doesn't scale. The real advantage that genAI brings to the table is that it "learns" in a way that can replicate this intuition and that it keeps scaling so long as you can shovel more compute and more data at it.
In a way, yes, you'd be paying the people not just to write down the rules but to discover them first. And there's the accuracy/correctness/interpretability tradeoff.
But also, have there been any attempts on the scale of the Manhattan project attempting to create a GOFAI?
Because one idea I ran into is that we might be able to use genAI to create a GOFAI soon. And it would be as hard as using genAI for any kind of large project. But I also can't convincingly claim that it's somehow provably impossible.
You can’t “write down the rules” for intelligence. Not for any reasonable definition of “writing”. The medium of writing is not rich enough to express what is needed.
Do you believe intelligence can be achieved using ANNs? If so, ANNs can be serialized, therefore writing is rich enough.
It might not be an easy to work with format though. If you believe the broad LLM architecture is capable of reaching true intelligence, then writing is still enough because all LLMs are is the written training data and the written training algorithm. It's just that is was impossible to pay people to write enough training data and provide enough compute to process it before.
