But a worker has more capacity than a donor does to learn whether small probability failure modes prevail in practice, and can switch to a different job if he or she finds that such a failure mode prevails.
This part seems exactly wrong. When GiveWell or Giving What We Can change their recommendations based on new data or arguments and explain their reasoning, the donations switch rapidly and en masse. EA donations have very little inertia.
Building an organization in a specific field, accumulating field-specific human capital (experience, CV, education), these involve putting years of effort into a particular project or vision. If you later find out that cancer biology was a bad move and you think that renewable energy is more important, your years doing a PhD in that area are now substantially wasted. Careers have very high inertia and investment in cause-specific capital, while earning power is flexible and donations can be highly responsive to new inputs.
I acknowledge that Jobs is a cherry picked example, but I think that the general principle still holds.
It is highly cherry-picked from two directions. Jobs gave up most of his Apple stock so that he captured a relatively small share of Apple’s recent rise, and he is generally believed to have had more irreplaceable impact on his company than virtually all CEOs (although still Apple stock did not plummet with his death).
Jobs’s death was known to be on the way. It would be surprising if the stock plummeted enough at that point to produce a predictable profit for someone shorting it.
This seems exactly wrong. When GiveWell or Giving What We Can change their recommendations based on new data or arguments and explain their reasoning, the donations switch rapidly and en masse. EA donations have very little inertia.
MacAskill mentioned this in his original article. My response was cost-effectiveness doesn’t vary as much as initially appears to be the case (though I recognize that my discussion is specific to global health).
Building an organization in a specific field, accumulating field-specific human capital (experience, CV, education), these involve putting years of effort into a particular project or vision. If you later find out that cancer biology was a bad move and you think that renewable energy is more important, your years doing a PhD in that area are now substantially wasted. Careers have very high inertia and investment in cause-specific capital, while earning power is flexible and donations can be highly responsive to new inputs.
I view this as more of an argument in favor of building transferable skills (rather than highly specialized skills) than an argument in favor of earning to give.
It is highly cherry-picked from two directions. Jobs gave up most of his Apple stock so that he captured a relatively small share of Apple’s recent rise, and he is generally believed to have had more irreplaceable impact on his company than virtually all CEOs (although still Apple stock did not plummet with his death).
I don’t mind having cherry-picked the example – I chose it to get people thinking rather than with the intent of weaving a tight argument.
I’d add that it isn’t obvious whether people working “in the field” are more attuned to small probability failure modes than, say GiveWell. One reason is that these people only tend to know about small probability failure modes within their own field, and certain very closely related fields. So they don’t have a strong basis for comparison. In addition, workers may only know about the low probability failure modes within their own part of the operation, so they may have less of a sense than charity evaluators of how it all hangs together.
I agree with this point as stated, but think that by thinking about how it all hangs together (or by listening to those who have) before choosing a career trajectory and by choosing a career that leaves sufficiently many options open, one can “have one’s cake and eat it too” — getting getting both the epistemic benefits from being on the ground and the epistemic benefits from looking at things in a broader way.
I agree. The best cogs understand their role in the machine, which requires intimate understanding of the machine as a whole. AND they can feel what’s going on as it happens.
This part seems exactly wrong. When GiveWell or Giving What We Can change their recommendations based on new data or arguments and explain their reasoning, the donations switch rapidly and en masse. EA donations have very little inertia.
Building an organization in a specific field, accumulating field-specific human capital (experience, CV, education), these involve putting years of effort into a particular project or vision. If you later find out that cancer biology was a bad move and you think that renewable energy is more important, your years doing a PhD in that area are now substantially wasted. Careers have very high inertia and investment in cause-specific capital, while earning power is flexible and donations can be highly responsive to new inputs.
It is highly cherry-picked from two directions. Jobs gave up most of his Apple stock so that he captured a relatively small share of Apple’s recent rise, and he is generally believed to have had more irreplaceable impact on his company than virtually all CEOs (although still Apple stock did not plummet with his death).
Jobs’s death was known to be on the way. It would be surprising if the stock plummeted enough at that point to produce a predictable profit for someone shorting it.
MacAskill mentioned this in his original article. My response was cost-effectiveness doesn’t vary as much as initially appears to be the case (though I recognize that my discussion is specific to global health).
I view this as more of an argument in favor of building transferable skills (rather than highly specialized skills) than an argument in favor of earning to give.
I don’t mind having cherry-picked the example – I chose it to get people thinking rather than with the intent of weaving a tight argument.
I’d add that it isn’t obvious whether people working “in the field” are more attuned to small probability failure modes than, say GiveWell. One reason is that these people only tend to know about small probability failure modes within their own field, and certain very closely related fields. So they don’t have a strong basis for comparison. In addition, workers may only know about the low probability failure modes within their own part of the operation, so they may have less of a sense than charity evaluators of how it all hangs together.
I agree with this point as stated, but think that by thinking about how it all hangs together (or by listening to those who have) before choosing a career trajectory and by choosing a career that leaves sufficiently many options open, one can “have one’s cake and eat it too” — getting getting both the epistemic benefits from being on the ground and the epistemic benefits from looking at things in a broader way.
I agree. The best cogs understand their role in the machine, which requires intimate understanding of the machine as a whole. AND they can feel what’s going on as it happens.