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From Ingar Haaland:
With math essentially being delegated to OpenAI, here’s what I predict for economics and the social sciences more generally:
The top tier of research will just become better and it will be normal human-led research where AI is used for scale (e.g. conducting qualitative interviews with relevant populations, running behavioral interventions in the field, analyzing massive text data, etc.)
Field experiments will rise in value and generally making “connections” with firms and being able to run stuff (potentially testing AI pipelines) will be in high demand
Research with administrative data will become even more valuable, but the benefits might be concentrated among some prolific authors who are allowed by the government agencies to use local models to analyze the data at scale. PhD students can probably forget about it
Economic history will have a very exciting boom and there will be an enormous race and big rewards for digitizing archives as a source to both identify new research strategies and document new descriptive facts.
Review and verification systems will become massively improved with AI assistance and it will become standard practice to do a 360 review of the paper + code + data at the *submission* stage.
Research that in principle can already be almost completely outsourced to the AI (download and analyze public data, run simple survey experiments, write theory models) is in for a big shock. This would be very bad from the perspective of researchers who depend on this “bread and butter” research, but I think this type of work will just be outsourced to public agencies who can answer their own questions without the need for “peer-reviewed” research.
All of those make sense to me.
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