Are there any notable differences in your ability to have impact in the different areas you conduct research? E.g. one area where important novel insights are easier / harder, or one area where relevant research is more easily translated into practice
Yes. I think animal welfare remains incredibly understudied and thus it is easier to have a novel insight, but also there is less literature to draw from and you can end up more fundamentally clueless. Whereas in global health and development work there is much more research to draw from, which makes it nicer to be able to do literature reviews to turn existing studies and evidence into grant recommendations, but also means that a lot of the low-hanging fruit has been done already.
Similarly, there is a lot more money available to chase top global health interventions relative to animal welfare or x-risk work, but it is also comparably harder to improve recommendations as a lot of the recommendations are already pretty well-known by foundations and policymakers.
AI has been an especially interesting place to work in because it has been rapidly mainstreaming this year. Previously, there was not much to draw on but now there is much more to draw from and many more people are open to being advised on work in the area. However, there are also many more people trying to get involved and work is being produced at a very rapid pace, which can make it harder to keep up and harder to contribute.
Are there any notable differences in your ability to have impact in the different areas you conduct research? E.g. one area where important novel insights are easier / harder, or one area where relevant research is more easily translated into practice
Yes. I think animal welfare remains incredibly understudied and thus it is easier to have a novel insight, but also there is less literature to draw from and you can end up more fundamentally clueless. Whereas in global health and development work there is much more research to draw from, which makes it nicer to be able to do literature reviews to turn existing studies and evidence into grant recommendations, but also means that a lot of the low-hanging fruit has been done already.
Similarly, there is a lot more money available to chase top global health interventions relative to animal welfare or x-risk work, but it is also comparably harder to improve recommendations as a lot of the recommendations are already pretty well-known by foundations and policymakers.
AI has been an especially interesting place to work in because it has been rapidly mainstreaming this year. Previously, there was not much to draw on but now there is much more to draw from and many more people are open to being advised on work in the area. However, there are also many more people trying to get involved and work is being produced at a very rapid pace, which can make it harder to keep up and harder to contribute.