In my somewhat analogous picture (which I’ve thought through on paper less than you), having AI workforce is more like
having more people
who can effectively think for longer between experiments
(The inverted slowcorp would have fewer people who are more distracted/have less time to think between experiments.)
To me that’s the natural operationalisation of this analogy. I notice it doesn’t obviously map directly to your analogy, where there are different quantities of compute, different serial research times, etc. Do you think your analogy is more natural, or does it in fact map more closely than I’m seeing?
In my somewhat analogous picture (which I’ve thought through on paper less than you), having AI workforce is more like
having more people
who can effectively think for longer between experiments
(The inverted slowcorp would have fewer people who are more distracted/have less time to think between experiments.)
To me that’s the natural operationalisation of this analogy. I notice it doesn’t obviously map directly to your analogy, where there are different quantities of compute, different serial research times, etc. Do you think your analogy is more natural, or does it in fact map more closely than I’m seeing?
Given the picture I’ve suggested, the relevant questions are
What are the returns to thinking longer for experiment design?
(I tentatively think they’re quite harsh)
What are the penalties for parallelised experiment design and/or for teamwork on experiment design?
(I’ve written even less on this, but I think it’s quite difficult because the key is pooling research taste, either from best-of-k or some team iterative thing, but research taste is mostly acquired through experimental experience)
(Perhaps also as you note, are there cognitive diversity penalties or bonuses at play?)