Comment by geokon

Working in geology, I find the opposite problem. Field work is so highly valued that we're at a place where we have so much data and not enough people really working and analyzing it. My general impression is that in some subfields work that's done exclusively using preexisting data is kind of looked down on. In my opinion tons and tons of money is essentially wasted collecting new data - and then it's poorly catalogued and hard to access. You typically have to email some author and hope they send you the data. People are fiercely protective of their data b/c it took a lot of effort to collect and they want credit and to be in on any derivative work (and not just a reference at the bottom of a paper)

I would say the main workflow is collect some new data nobody has collect before, look at it and see if it shows anything interesting, make up some interesting publishable interpretation.

It feels like it'd be smarter to start with working with existing data and publish that way. If you hit on some specific missing piece, go collect that data, and work from there. But the incentive structures aren't aligned with this

The AI angle is really shoehorned in, but irrelevant to the larger problem. Sure, it allows you to annotate more data. Obviously it's more fun to go do field work than count pollen grains under a microscope. If anything AI make it easier to do more fieldwork and collect even more data b/c now you can in-theory crunch it faster