I agree that superlinearity is way more pronounced in some cases than in others.
However, I still think there can be some superlinear terms for things that aren’t inherently about speed. E.g. climbing seniority levels or getting a good reputation with ever larger groups of people.
The examples you give fit my notion of speed—you’re trying to make things happen faster than the people with whom you’re competing for seniority/reputation.
Similarly, speed matters in quant trading not primarily because of real-world influence on the markets, but because you’re competing for speed with other traders.
Fair, that makes sense! I agree that if it’s purely about solving a research problem with long timelines, then linear or decreasing returns seem very reasonable.
I would just note that speed-sensitive considerations, in the broad sense you use it, will be relevant to many (most?) people’s careers, including researchers to some extent (reputation helps doing research: more funding, better opportunities for collaboration etc). But I definitely agree there are exceptions and well-established AI safety researchers with long timelines may be in that class.
FWIW I think superlinear returns are plausible even for research problems with long timelines, I’d just guess that the returns are less superlinear, and that it’s harder to increase the number of work hours for deep intellectual work. So I quite strongly agree with your original point.
I agree that superlinearity is way more pronounced in some cases than in others.
However, I still think there can be some superlinear terms for things that aren’t inherently about speed. E.g. climbing seniority levels or getting a good reputation with ever larger groups of people.
The examples you give fit my notion of speed—you’re trying to make things happen faster than the people with whom you’re competing for seniority/reputation.
Similarly, speed matters in quant trading not primarily because of real-world influence on the markets, but because you’re competing for speed with other traders.
Fair, that makes sense! I agree that if it’s purely about solving a research problem with long timelines, then linear or decreasing returns seem very reasonable.
I would just note that speed-sensitive considerations, in the broad sense you use it, will be relevant to many (most?) people’s careers, including researchers to some extent (reputation helps doing research: more funding, better opportunities for collaboration etc). But I definitely agree there are exceptions and well-established AI safety researchers with long timelines may be in that class.
FWIW I think superlinear returns are plausible even for research problems with long timelines, I’d just guess that the returns are less superlinear, and that it’s harder to increase the number of work hours for deep intellectual work. So I quite strongly agree with your original point.