And I asked some friends what “hardware overhang” means and they had different responses (a plurality said it means sufficient hardware for human-level AI already exists, which is not a useful concept).
It’s not a useful concept if we can’t talk about the probability of “finding” particularly efficient AGI architectures through new insights. However, it seems intelligible and strategically important to talk about something like “the possibility that we’re one/a few easy-to-find insight(s) away from suddenly being able to build AGI with a much smaller compute budget than the largest training runs to date.” That’s a contender for the concept of “compute overhang.” (See also my other comment.)
Maybe what you don’t like about this definition is that it’s inherently fuzzy: even if we knew everything about all possible AGI architectures, we’d still have uncertainty about how long it’ll take AI researchers to come up with the respective insights. I agree that this makes the concept harder to reason about (and arguably less helpful).
It’s not a useful concept if we can’t talk about the probability of “finding” particularly efficient AGI architectures through new insights. However, it seems intelligible and strategically important to talk about something like “the possibility that we’re one/a few easy-to-find insight(s) away from suddenly being able to build AGI with a much smaller compute budget than the largest training runs to date.” That’s a contender for the concept of “compute overhang.” (See also my other comment.)
Maybe what you don’t like about this definition is that it’s inherently fuzzy: even if we knew everything about all possible AGI architectures, we’d still have uncertainty about how long it’ll take AI researchers to come up with the respective insights. I agree that this makes the concept harder to reason about (and arguably less helpful).