AI will know almost all of the academic literature, and will be better at modeling and solving most of the quantitative problems. It will be better at specifying the model and running through the actual statistical exercises. Humans likely will oversee these functions, but most of that will consist of nodding, or trying out some different prompts.
The humans will gather the data. They will do the lab work, or approach the companies (or the IRS?) to access new data. They will be the ones who pledge confidentiality and negotiate terms of data access. (Though an LLM might write the contract.) They will know someone, somewhere, using a telescope to track a particular quasar. They may (or may not) know that the AI’s suggestion to sample the blood of a different kind of gila monster is worth pursuing. They will decide whether we should be filming dolphins or whales, so that we may talk to them using LLMs, though they will ask the LLMs for cost estimates in each case.
At least in economics, this will be continuing trends that were present before current high-quality AI. The scarce input behind a quality paper is, more and more, access to some new and interesting data source. More and more people can do the requisite follow-up technical work, though quality variations have by no means been eliminated.
“Science as an employment program for scientists” will fall all the more out of favor. It remains to be seen how much that will disfavor serendipitous human discovery.
On any given day, on the quest for more data, a scientist will have to think quite creatively about what he or she should be doing.
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