Here is my two sides argument concerning AI in sciences (from a former biophysicist). Positive side: in sciences, you need to have a lot of transverse skills s

Here is my two sides argument concerning AI in sciences (from a former biophysicist). Positive side: in sciences, you need to have a lot of transverse skills such as presentation, data curation and data analysis. In biology in particular, students are never taught how to properly store data and even less how to automate the analysis. The pattern I saw was always the same: the data is stored in a convenient way for humans to read, but impossible to process via a script (think patches of columns in separate excel sheets, sometimes the labels are on the side of the value instead of on top). I also was guilty of this at the start of my PhD and had to learn it. With coding agent, you don't have that coding barrier anymore and can plot your data and even get coaching on how to properly store your data so I think this is a huge boon. Second positive side is that you can get insight on papers which are a bit outside of your field and extract some information. Negative side: AI also allows you to push more garbage, especially if you are working in a less experiment-heavy field, so it will get harder to sift through good and bad papers because now you can make a bad paper look good. This is honestly more of a deeper problem with sciences in general that has the unfortunate publish-or-die mindset where grants are given based on how many publications you have rather than the quality and impact of your publications. This will only amplify the problem as now you can spit out papers faster, meaning the competition can spit out papers faster... Given how many people eventually quit after their PhD or first post-doc (that's me), I find it hard to argue that we need more scientists in the current academic environnement.