I think that this: > but the intuition that calls this model naive is driven by a sense that it’s going to turn out to not “actually” be 2 additional people, that additionality is going to be lower than you think, that the costs of getting that result are higher than you think, etc. etc.
is most of the answer. Getting a fully counterfactual career shift (that person’s expected career value without your intervention is ~0, but instead they’re now going to work at [job you would otherwise have taken, for at least as long as you would have]) is a really high bar to meet. If you did expect to get 2 of those, at equal skill levels to you, then I think the argument for ‘going meta’ basically goes through.
In practice, though: - People who fill [valuable role] after your intervention probably had a significant chance of finding out about it anyway. - They also probably had a significant chance of ending up in a different high-value role had they not taken the one you intervened on.
How much of a discount you want to apply for these things is going depend a lot on how efficiently you expect the [AI safety] job market to allocate talent. In general, I find it easier to arrive at reasonable-seeming estimates for the value of career/trajectory changes by modelling them as moving the the change earlier in time rather than causing it to happen at all. How valuable you expect the acceleration to be depends on your guesses about time-discounting, which is another can of worms, but I think is plausibly significant, even with no pure rate of time preference.
(This is basically your final bullet, just expanded a bit.)
I think that this:
> but the intuition that calls this model naive is driven by a sense that it’s going to turn out to not “actually” be 2 additional people, that additionality is going to be lower than you think, that the costs of getting that result are higher than you think, etc. etc.
is most of the answer. Getting a fully counterfactual career shift (that person’s expected career value without your intervention is ~0, but instead they’re now going to work at [job you would otherwise have taken, for at least as long as you would have]) is a really high bar to meet. If you did expect to get 2 of those, at equal skill levels to you, then I think the argument for ‘going meta’ basically goes through.
In practice, though:
- People who fill [valuable role] after your intervention probably had a significant chance of finding out about it anyway.
- They also probably had a significant chance of ending up in a different high-value role had they not taken the one you intervened on.
How much of a discount you want to apply for these things is going depend a lot on how efficiently you expect the [AI safety] job market to allocate talent. In general, I find it easier to arrive at reasonable-seeming estimates for the value of career/trajectory changes by modelling them as moving the the change earlier in time rather than causing it to happen at all. How valuable you expect the acceleration to be depends on your guesses about time-discounting, which is another can of worms, but I think is plausibly significant, even with no pure rate of time preference.
(This is basically your final bullet, just expanded a bit.)