Comment senior scientist apples vs early career pears (Score 1) 64
So what i gather is that a subset of science benefits from AI that does the data analysis. The vast majority doesn't when doing the data analysis. The LLM is not going to perform the enzyme assay or clone the gene, or inject the drug into the mouse. This subset of science clusters more tightly around popular, data-rich problems.
This is my opinion: once you are some sort of senior researcher who leads a very big consortium that produces lots of data, you use AI to process all that data and that very big study gets you lots of papers and advances your career immensely (this seems logical to me), but there is not a lot of follow-up as this is likely the end stage of a line of research. If you do smaller studies (maybe earlier in your career?) it doesn't go that fast (this makes sense to me as well), but all these small studies may lead up to a really big idea with lots of research, resulting in more followup. (and finally leading to the massive consortium with tons of data, needing AI methods again).
This is my opinion: once you are some sort of senior researcher who leads a very big consortium that produces lots of data, you use AI to process all that data and that very big study gets you lots of papers and advances your career immensely (this seems logical to me), but there is not a lot of follow-up as this is likely the end stage of a line of research. If you do smaller studies (maybe earlier in your career?) it doesn't go that fast (this makes sense to me as well), but all these small studies may lead up to a really big idea with lots of research, resulting in more followup. (and finally leading to the massive consortium with tons of data, needing AI methods again).
