Thank you for the pointer! I hadn’t seen this before and it looks like there’s a lot of interesting thinking on how to study AI safety field building. I appreciate having more cost-effectiveness estimates to compare to.
I haven’t given it a full read, but it seems like the quality-adjusted researcher year is very similar to the metric I’m proposing here.
To do a comparison between our estimates, lets assume a new AIS researcher does 10 years of quality adjusted, time-discounted AIS research (note that timelines become pretty important here) then we get:
(10 QARY’s/researcher) / ($30K/researcher) = 3.33E-4 QURY’s per dollar = 333 QURY’s per $1M
That seems similar to the CAIS estimates for MLSS, so it seems like these approaches have pretty comparable results!
In the future I’m also interested in modelling how to distribute funding optimally in talent pipelines like these.
I think 333 QARYs/$1m via the CAIS framework is significantly too optimistic, for two reasons:
The CAIS framework would probably make several adjustments downwards that you have not considered here, in particular for scientist-equivalence (where research engineers are valued at 0.1x research scientists).
At the 20% time discount rate that CAIS uses for default estimates, 10 years of time-discounted research is implausible (the infinite geometric sum of time-discounted years is equal to 5 non-discounted years).
Thanks for the clarification, a 20% changes things a lot, I’ll have to read into why they chose that.
Let’s try to update it. I’m not sure how to categorize different roles into scientists vs engineers, but eyeballing the list of positions AISC participants got, assume half become scientists and disregard the contributions of research engineers. With a 20% discount rate, 10 years of work in a row is more like 4.5. so we get:
333 * 0.45 / 2= ~75 QARY’s / $1M
The real number would be lower since AISC focuses on new researchers who have a delay in their time to entering the field, e.g. a 3 year delay would halve this value.
Thank you for the pointer! I hadn’t seen this before and it looks like there’s a lot of interesting thinking on how to study AI safety field building. I appreciate having more cost-effectiveness estimates to compare to.
I haven’t given it a full read, but it seems like the quality-adjusted researcher year is very similar to the metric I’m proposing here.
To do a comparison between our estimates, lets assume a new AIS researcher does 10 years of quality adjusted, time-discounted AIS research (note that timelines become pretty important here) then we get:
(10 QARY’s/researcher) / ($30K/researcher) = 3.33E-4 QURY’s per dollar = 333 QURY’s per $1M
That seems similar to the CAIS estimates for MLSS, so it seems like these approaches have pretty comparable results!
In the future I’m also interested in modelling how to distribute funding optimally in talent pipelines like these.
I think 333 QARYs/$1m via the CAIS framework is significantly too optimistic, for two reasons:
The CAIS framework would probably make several adjustments downwards that you have not considered here, in particular for scientist-equivalence (where research engineers are valued at 0.1x research scientists).
At the 20% time discount rate that CAIS uses for default estimates, 10 years of time-discounted research is implausible (the infinite geometric sum of time-discounted years is equal to 5 non-discounted years).
Thanks for the clarification, a 20% changes things a lot, I’ll have to read into why they chose that.
Let’s try to update it. I’m not sure how to categorize different roles into scientists vs engineers, but eyeballing the list of positions AISC participants got, assume half become scientists and disregard the contributions of research engineers. With a 20% discount rate, 10 years of work in a row is more like 4.5. so we get:
333 * 0.45 / 2= ~75 QARY’s / $1M
The real number would be lower since AISC focuses on new researchers who have a delay in their time to entering the field, e.g. a 3 year delay would halve this value.