In most cases you can continuously trade off performance and cost; for that reason I usually think of them as a single metric of “competitive with X% overhead.” I agree there are cases where they come apart, but I think there are pretty few examples. (Even for nuclear weapons you could ask “how much more expensive is it to run a similarly-destructive bombing campaign with conventional explosives.”)
I think this works best if you consider a sequence of increments each worth +10%, rather than say accumulating 70 of those increments, because “spend 1000x more” is normally not available and so we don’t have a useful handle on what a technology looks like when scaled up 1000x (and that scaleup would usually involve a bunch of changes that are hard to anticipate).
That is, if we have a sequence of technologies A0, A1, A2, …, AN, each of which is 10% cheaper than the one before, then we may say that AN is better than A0 by N 10% steps (rather than trying to directly evaluate how many orders of magnitude you’d have to spend on A0 to compete with AN, because the process “spend a thousand times more on A0 in a not-stupid way” is actually kind of hard to imagine).
I agree this may be true in most cases, but the chance of it not being true for AI is large enough to motivate the distinction. Besides, not all cases in which performance and cost can be traded off are the same; in some scenarios the “price” of performance is very high whereas in other scenarios it is low. (e.g. in Gradual Economic Takeover, let’s say, a system being twice as qualitatively intelligent could be equivalent to being a quarter the price. Whereas in Final Conflict, a system twice as qualitatively intelligent would be equivalent to being one percent the price.) So if we are thinking of a system as “competitive with X% overhead,” well, X% is going to vary tremendously depending on which scenario is realized. Seems worth saying e.g. “costs Y% more compute, but is Z% more capable.”
In most cases you can continuously trade off performance and cost; for that reason I usually think of them as a single metric of “competitive with X% overhead.” I agree there are cases where they come apart, but I think there are pretty few examples. (Even for nuclear weapons you could ask “how much more expensive is it to run a similarly-destructive bombing campaign with conventional explosives.”)
I think this works best if you consider a sequence of increments each worth +10%, rather than say accumulating 70 of those increments, because “spend 1000x more” is normally not available and so we don’t have a useful handle on what a technology looks like when scaled up 1000x (and that scaleup would usually involve a bunch of changes that are hard to anticipate).
That is, if we have a sequence of technologies A0, A1, A2, …, AN, each of which is 10% cheaper than the one before, then we may say that AN is better than A0 by N 10% steps (rather than trying to directly evaluate how many orders of magnitude you’d have to spend on A0 to compete with AN, because the process “spend a thousand times more on A0 in a not-stupid way” is actually kind of hard to imagine).
I agree this may be true in most cases, but the chance of it not being true for AI is large enough to motivate the distinction. Besides, not all cases in which performance and cost can be traded off are the same; in some scenarios the “price” of performance is very high whereas in other scenarios it is low. (e.g. in Gradual Economic Takeover, let’s say, a system being twice as qualitatively intelligent could be equivalent to being a quarter the price. Whereas in Final Conflict, a system twice as qualitatively intelligent would be equivalent to being one percent the price.) So if we are thinking of a system as “competitive with X% overhead,” well, X% is going to vary tremendously depending on which scenario is realized. Seems worth saying e.g. “costs Y% more compute, but is Z% more capable.”