Can you give more details what “distributing resources over a wider group of people” means for you?
Are you arguing that mentors should spend much less time per person and instead mentor 3 times as many people?
Are you arguing that researchers should get half as much money so twice as many researchers can get funded?
A plausible hypothesis is that ordinary methods of distributing resources over a wider group of people don’t unlock that many additional researchers.
Then, if there is only infrastructure that can support a limited number of people, then it is not very surprising to me that there is a focus on so-called ‘top talent’. All else being equal, you would rather have competent people. And there is probably not some central EA decision that favors a small number of researchers over a large number of researchers.
Some side remark:
For example in AI safety there are a few very well funded but extremely competitive programmes for graduates who want to do research in the field. Naturally their output is then limited to relatively small groups of people.
Naming the specific programmes might give you better answers here. People who want to answer have to speculate less, and if you are lucky the organizers of specific orgs might be inclined to answer.
An opposing trend seems to have gained traction in AI capability research as e.g. the “We have no moat” paper argued, where a load of output comes from the sheer mass of people working on the problem with a breadth-first approach.
Yes indeed that’s what I am suggesting: if a strong bottleneck is mentoring for an org, one approach of “more broadly distributing ressources” might be that programmes increase their student-staff ratio (meaning a bit more self-guided work for each participant but more participants in total)
Prominent and very competitive programmes I was thinking of are SERI MATS and MLAB from redwood, but I think that extreme applicants-participant ratios are true for pretty much all paid and even many non-paid EA fellowships, e.g. PIBBSS or . Thanks for the hint that it may be helpful to mention some of them.
@‘they have more ressources than us’: Why does that matter? If the question is “How can we achieve the most possible impact with the limited ressources we got?”. Then given the extreme competitiveness of these programmes and the early-career-stage most applicants are in, a plausible hypothesis is that scaling up the quantity of these training programmes at the expense of quality is a way to increase total output. And so far it seems to me that this is potentially neglected
Can you give more details what “distributing resources over a wider group of people” means for you? Are you arguing that mentors should spend much less time per person and instead mentor 3 times as many people? Are you arguing that researchers should get half as much money so twice as many researchers can get funded?
A plausible hypothesis is that ordinary methods of distributing resources over a wider group of people don’t unlock that many additional researchers. Then, if there is only infrastructure that can support a limited number of people, then it is not very surprising to me that there is a focus on so-called ‘top talent’. All else being equal, you would rather have competent people. And there is probably not some central EA decision that favors a small number of researchers over a large number of researchers.
Some side remark:
Naming the specific programmes might give you better answers here. People who want to answer have to speculate less, and if you are lucky the organizers of specific orgs might be inclined to answer.
They have much more resources than us.
Yes indeed that’s what I am suggesting: if a strong bottleneck is mentoring for an org, one approach of “more broadly distributing ressources” might be that programmes increase their student-staff ratio (meaning a bit more self-guided work for each participant but more participants in total)
Prominent and very competitive programmes I was thinking of are SERI MATS and MLAB from redwood, but I think that extreme applicants-participant ratios are true for pretty much all paid and even many non-paid EA fellowships, e.g. PIBBSS or . Thanks for the hint that it may be helpful to mention some of them.
@‘they have more ressources than us’: Why does that matter? If the question is “How can we achieve the most possible impact with the limited ressources we got?”. Then given the extreme competitiveness of these programmes and the early-career-stage most applicants are in, a plausible hypothesis is that scaling up the quantity of these training programmes at the expense of quality is a way to increase total output. And so far it seems to me that this is potentially neglected